Analysis of DataSet GSE186063
GONÇALVES, L.C.
2025-10-28
1. Installing and Loading Packages
This first block of code installs and loads all the libraries needed
for the analysis. The include=FALSE option in the block
header ensures that the installation commands and loading messages will
not appear in the final HTML report, making it cleaner.
2. Data Loading and Preparation
Here, we load the raw count table and gene annotations directly from the GEO repository.
# Load GEO score table
urld <- "https://www.ncbi.nlm.nih.gov/geo/download/?format=file&type=rnaseq_counts"
path <- paste(urld, "acc=GSE186063", "file=GSE186063_raw_counts_GRCh38.p13_NCBI.tsv.gz", sep="&");
gset<- as.matrix(data.table::fread(path, header=T, colClasses="integer"), rownames="GeneID")
# Load gene annotations
apath <- paste(urld, "type=rnaseq_counts", "file=Human.GRCh38.p13.annot.tsv.gz", sep="&")
annot <- data.table::fread(apath, header=T, quote="", stringsAsFactors=F, data.table=F)
# Load the dataset
gse <- getGEO("GSE186063", GSEMatrix = TRUE, AnnotGPL = TRUE)## Found 1 file(s)## GSE186063_series_matrix.txt.gz## Annotation GPL not available, so will use submitter GPL insteadgse <- gse[[1]]
# Extract phenotype data
pheno_data <- pData(gse)
pheno_data <- pheno_data[, c("title", "geo_accession", "diagnosis:ch1", "source_name_ch1")]3. Selection of 433 genes
Gene selection performed by text mining and functional enrichment.
#Define genes of interest
genes_of_interest <- c(
"ABCB11", "ABCD2", "ACE", "ACKR2", "ACP5", "ACP7", "ADAMTS9", "ADCY7", "ADGRL2", "ADIPOQ", "AHR", "AIM2", "AKAP13", "ALB", "ANKRD55", "ANXA1",
"APOL1", "APOL6", "AQP1", "AREG", "ARHGEF3", "ATG16L1", "ATG5", "ATOX1", "ATXN2L", "BEND2", "BGLAP", "BMP2", "BMP7", "BNIP3L", "BRAF", "BTG1",
"C15ORF48", "C7ORF57", "CALD1", "CAPS2", "CARD9", "CASP1", "CASP10", "CAST", "CCL2", "CCL20", "CCL25", "CCL3", "CCL5", "CCND1", "CCND3", "CCR2",
"CCR6", "CCR7", "CCRL2", "CD14", "CD160", "CD19", "CD274", "CD28", "CD36", "CD38", "CD3E", "CD4", "CD40", "CD40LG", "CD52", "CD63", "CD68",
"CD69", "CD80", "CD83", "CD86", "CD8A", "CD8B", "CDC42BPB", "CEBPA", "CEBPG", "CENPK", "CLEC2B", "CLEC4D", "CLIC3", "CMAHP", "CMTM2",
"COL1A1", "COX2", "CRB1", "CREM", "CRP", "CSF2", "CSMD1", "CSN3", "CSNK1A1", "CTLA4", "CTSB", "CTSK", "CUX1", "CX3CR1", "CXCL10", "CXCL13",
"CXCL16", "CXCL2", "CXCL8", "CXCR2", "CYCS", "CYP1A1", "CYP4F22", "DDIT3", "DDX60", "DKK1", "DLAT", "DNAJA2", "DNAJB6", "DUSP4", "DYSF",
"EDNRA", "EFCAB13", "EFCAB7", "EGF", "EGFR", "EGR3", "EIF5B", "ENO1", "EOMES", "ERAP1", "ERAP2", "ERN1", "ERP44", "EZH2", "FAXDC2", "FCGR1A",
"FCGR2A", "FCGR3A", "FGB", "FNBP1", "FOS", "FOSL1", "FOSL2", "FOXP3", "FRZB", "FUT2", "GAPDH", "GATA3", "GBP1", "GBP3", "GBP5", "GEM", "GINS1",
"GJB2", "GJB6", "GLUL", "GOLIM4", "GPR35", "GPT", "GZMA", "GZMB", "GZMK", "HCAR3", "HERC6", "HHAT", "HIF1A", "HK2", "HLA-A", "HLA-B", "HLA-C",
"HLA-DQB1", "HLA-DRB1", "HSPA5", "HSPA6", "HYAL4", "ICAM1", "ICOS", "IFI16", "IFI6", "IFIH1", "IFIT3", "IFNA1", "IFNG", "IFNGR1", "IFNLR1",
"IGF2", "IGF2-AS", "IL10", "IL12B", "IL13", "IL15", "IL17A", "IL17F", "IL17RA", "IL18", "IL1A", "IL1B", "IL1F10", "IL1R1", "IL1RN", "IL2",
"IL21", "IL21-AS1", "IL22", "IL23A", "IL23R", "IL2RA", "IL33", "IL36RN", "IL37", "IL4", "IL5", "IL6", "IL6R", "IL7", "INS", "INS-IGF2", "IRAK1",
"IRS1", "ITGA2B", "ITGAL", "ITGAM", "ITGAX", "JAK2", "JAK3", "JDP2", "JRKL", "JUN", "JUNB", "JUND", "KANK4", "KDM5B", "KIR2DS1", "KIR3DL1",
"KIR3DL2", "KLRB1", "LAMP1", "LAMP2", "LEP", "LILRA5", "LILRB2", "LINC01185", "LINC01250", "LMO7", "LPAL2", "LRRK2", "LTA", "LURAP1L",
"LURAP1L-AS1", "LYZ", "MAF", "MBL2", "MCAM", "MCL1", "MEFV", "MICA", "MIR146A", "MIR21", "MIX23", "MMP1", "MMP3", "MMP7", "MMP9", "MRPS23",
"MSN", "MTHFR", "MUCL1", "MX1", "MYC", "MYNN", "NABP1", "NAMPT", "NCOA7", "NDUFS1", "NFKB1", "NKG7", "NLRP3", "NMI", "NOD2", "NOS2",
"NOXRED1", "NPEPPS", "NRG1", "NT5C3A", "OLR1", "OSMR", "PDCD1", "PDLIM7", "PER1", "PFDN4", "PFDN5", "PFKL", "PGD", "PGK1", "PHEX", "PI3",
"PIK3CD", "PINK1", "PLA2G4D", "PLCG1", "PLG", "PLIN5", "PLS1", "PPARD", "PPARG", "PPARGC1A", "PPARGC1B", "PRDM1", "PRF1", "PRTN3", "PSG2",
"PSMC2", "PSMD7", "PSME2", "PTGS1", "PTGS2", "PTH", "PTPN22", "PTX3", "PYGL", "RAC1", "RBM45", "REL", "RETN", "RGPD6", "RIT1", "RORC", "RPL15",
"RPL36AL", "RPL41", "RPL7", "RPS19", "RPS21", "RPS26", "RPS6KB1", "RPS7", "RSAD2", "RUNX2", "RUNX3", "S100A12", "S100A8", "S100A9", "S100P",
"SAA1", "SAMD9", "SAR1A", "SCN1A", "SEC14L2", "SEC24B", "SELL", "SERPINA1", "SERPINB1", "SERPINE1", "SF3B1", "SF3B3", "SGK1", "SH3BGRL3",
"SIAH1", "SLC1A2", "SLC2A3", "SLC51B", "SLC7A11", "SLC7A5", "SMAD3", "SMARCA4", "SMOX", "SOCS1", "SOD2", "SOST", "SOX4", "SP7", "SPCS3",
"SPON2", "SPP1", "SSR1", "STAT1", "STAT3", "STIM1", "SUOX", "SYT1", "TALDO1", "TBX21", "TEK", "TFPI", "TGFA", "TGFB1", "TGFBR3", "TIMP1",
"TLR2", "TLR3", "TLR4", "TLR9", "TMBIM6", "TMEM45A", "TMPRSS11B", "TNF", "TNFAIP3", "TNFAIP6", "TNFAIP8", "TNFRSF10A", "TNFRSF1A",
"TNFRSF1B", "TNFRSF9", "TNFSF10", "TNFSF11", "TNFSF13B", "TNFSF15", "TNIP1", "TOMM5", "TOMM7", "TPST2", "TPT1", "TRAF2", "TRAF3IP2", "TRAF4",
"TRAF5", "TRBV20OR9-2", "TRIM22", "TRIM69", "TTC39B", "TYK2", "TYMP", "UBE2L3", "UQCR10", "UTP11", "VCAM1", "VDR", "VEGFA", "VIM", "WDR1",
"WNK1", "WWOX", "XAF1", "XBP1", "YOD1", "YWHAB", "ZC3H12A", "ZFP36", "ZMIZ1", "ZNF316", "ZNF415", "ZNF483"
)
# Load the data.table package
library(data.table)
# Keep only the notes for the genes of interest.
annot_filtered <- annot[annot$Symbol %in% genes_of_interest, ]
# Filter the count matrix using GeneIDs as the key (MUCH MORE SECURE)
expr_data_filtered <- gset[rownames(gset) %in% annot_filtered$GeneID, ]
# Filter expression data for genes of interest
express <- rownames(expr_data_filtered)
# Mapping Entrez IDs to gene symbols
gene_symbols <- mapIds(org.Hs.eg.db, keys = rownames(expr_data_filtered), column = "SYMBOL", keytype = "ENTREZID", multiVals = "first")## 'select()' returned 1:1 mapping between keys and columns# Replace rownames with gene symbols
rownames(expr_data_filtered) <- gene_symbols
# View the updated dataframe
print("Data frame com rownames modificados para símbolos de genes:")## [1] "Data frame com rownames modificados para símbolos de genes:"print(expr_data_filtered)## GSM5629971 GSM5629972 GSM5629973 GSM5629974 GSM5629975 GSM5629976
## TNFRSF9 50 3 13 28 226 12
## ENO1 21517 9106 21132 18757 21238 19317
## PIK3CD 491 284 584 344 412 393
## PGD 3456 2524 2100 1472 1664 2020
## MTHFR 1060 507 939 665 731 654
## TNFRSF1B 1522 477 1075 644 1401 884
## PINK1 1388 1164 2344 1475 1376 2275
## IFNLR1 194 222 508 333 225 371
## RUNX3 1255 745 1978 2018 903 1513
## SH3BGRL3 4651 1901 3289 1491 5552 3202
## CD52 833 282 290 109 972 219
## IFI6 5034 369 995 1873 8368 937
## ZC3H12A 3980 386 547 460 1318 549
## UTP11 1222 584 951 528 1194 764
## JUN 1725 886 2180 1888 3261 2115
## KANK4 43 74 84 135 5 127
## EFCAB7 116 148 255 182 168 199
## IL23R 15 3 2 1 0 3
## ADGRL2 957 468 747 687 589 749
## GBP3 1429 381 692 337 1082 390
## GBP1 2993 441 573 464 8242 717
## GBP5 535 71 135 99 1030 179
## TGFBR3 1977 1607 3344 2129 860 2725
## VCAM1 593 170 324 142 959 330
## PTPN22 146 39 50 37 218 53
## CD160 18 13 19 21 15 20
## FCGR1A 88 9 11 7 117 9
## MCL1 6253 2815 5078 4115 9390 5234
## CTSK 9277 2985 8517 4164 16149 16807
## RORC 191 516 1280 694 143 726
## S100A9 348987 955 265 258 100573 267
## S100A12 1383 4 8 1 45 2
## S100A8 332188 2023 151 703 94403 316
## IL6R 642 350 764 496 895 610
## RIT1 712 269 605 464 784 559
## BGLAP 19 12 49 32 22 25
## IFI16 6282 2358 3302 2500 5130 2969
## AIM2 81 7 15 7 197 29
## CRP 0 0 0 0 0 1
## FCGR2A 305 75 141 109 579 234
## HSPA6 327 112 141 105 345 138
## FCGR3A 393 19 72 12 638 51
## SELL 232 29 84 28 319 89
## GLUL 15899 5989 11400 8059 10581 10486
## PTGS2 58 16 21 30 141 53
## CRB1 0 3 8 7 2 19
## KDM5B 1834 1348 2331 1351 1237 1691
## IL10 10 4 2 6 23 10
## YOD1 1154 341 558 220 1086 420
## HHAT 171 180 226 145 142 226
## TRAF5 299 176 422 283 405 454
## NLRP3 82 24 33 26 89 63
## LINC01250 0 1 0 0 3 2
## RPS7 40511 21792 33674 13292 28269 19242
## RSAD2 420 75 144 133 857 139
## FOSL2 5364 2081 4251 3443 3101 4891
## REL 2081 1014 1504 1072 1230 1106
## TGFA 1015 261 581 382 798 389
## DYSF 721 152 352 321 313 509
## HK2 2276 562 793 664 1335 750
## CD8A 503 139 89 62 533 63
## CD8B 230 72 79 32 204 35
## EIF5B 6100 6040 10250 3943 3310 4873
## IL1R1 2777 1279 2702 1680 1706 2279
## RGPD6 319 374 745 523 366 570
## IL1A 17 3 25 11 31 20
## IL1B 80 15 7 6 81 7
## IL37 97 159 765 34 89 297
## IL36RN 7721 213 1169 45 2673 547
## IL1F10 44 13 40 0 33 44
## IL1RN 1743 1081 2271 909 2206 1295
## NMI 1513 406 671 364 2232 540
## TNFAIP6 78 43 41 66 278 177
## IFIH1 1083 359 549 476 1291 397
## SCN1A 7 12 1 5 18 21
## ABCB11 29 71 45 18 4 26
## RBM45 182 150 202 144 161 201
## FRZB 543 476 2404 1361 714 2110
## TFPI 634 292 771 307 575 704
## STAT1 9452 1246 1844 1717 14555 2363
## NABP1 364 146 213 151 601 252
## SF3B1 5807 4764 8360 6821 5949 7962
## CASP10 569 209 458 311 528 426
## CD28 198 39 38 30 301 65
## CTLA4 67 13 22 29 350 27
## ICOS 71 3 9 8 267 9
## NDUFS1 2390 1623 2785 1952 1939 2320
## CXCR2 532 110 204 78 80 138
## IRS1 634 238 502 465 239 784
## CCL20 79 7 2 30 36 9
## ATG16L1 606 334 876 622 762 770
## GPR35 43 16 65 71 36 56
## PDCD1 55 7 8 12 97 8
## PPARG 182 549 318 245 129 378
## RPL15 40705 24784 49015 23424 33900 33086
## EOMES 34 18 27 11 57 22
## CX3CR1 209 56 106 109 148 118
## ACKR2 169 10 16 20 72 65
## CCR2 340 108 113 118 358 178
## CCRL2 51 13 34 18 48 26
## TLR9 27 4 11 13 22 23
## ARHGEF3 1188 576 788 745 1283 1059
## ADAMTS9 338 100 212 238 118 262
## TMEM45A 11615 4899 6159 1058 9951 4107
## CD80 18 2 3 4 50 7
## CD86 219 82 102 75 361 122
## MIX23 365 202 340 173 309 222
## PLS1 139 69 186 198 75 230
## PTX3 96 18 81 50 23 58
## GOLIM4 1334 544 1393 1287 872 2151
## MYNN 710 308 583 412 999 576
## TNFSF10 4857 1909 3406 2377 7441 2496
## ADIPOQ 632 5 1062 1316 19 1972
## SPON2 1090 332 1477 1218 841 1506
## S100P 2056 159 1625 921 313 1179
## WDR1 8397 2726 6846 5170 7197 6944
## CD38 54 9 7 12 53 28
## PPARGC1A 429 372 1180 1188 124 1059
## TMPRSS11B 0 0 0 0 0 0
## CSN3 0 0 0 1 0 0
## ALB 2 3 10 5 1 8
## CXCL8 700 3 5 2 163 8
## CXCL2 88 5 1 3 75 13
## AREG 244 57 138 168 432 90
## CXCL10 707 32 11 13 13153 23
## CXCL13 97 2 12 12 374 9
## SPP1 85 5 3 12 111 434
## HERC6 1363 191 354 339 1848 329
## NFKB1 2457 1010 1957 1362 2084 1925
## SEC24B 957 687 1305 888 839 1201
## EGF 61 17 140 168 12 109
## IL2 1 3 0 0 5 0
## IL21 0 0 0 0 25 0
## IL21-AS1 0 0 0 0 2 0
## SLC7A11 202 24 28 3 62 54
## IL15 163 63 127 78 287 92
## EDNRA 636 264 517 634 371 446
## TLR2 259 117 243 193 282 193
## FGB 0 0 0 0 0 0
## DDX60 1919 313 675 436 2430 536
## SPCS3 2798 1306 1926 1266 2959 1876
## TLR3 209 75 137 98 224 170
## OSMR 1408 398 612 633 934 722
## GZMK 165 26 47 16 338 48
## GZMA 445 44 53 20 320 62
## ANKRD55 12 11 5 6 23 10
## CENPK 285 44 95 107 221 65
## CAST 8173 5028 9871 6765 6829 8803
## ERAP1 2395 1289 3103 1818 2842 2825
## ERAP2 256 191 314 1086 417 225
## TNFAIP8 1238 650 897 711 1821 931
## CSF2 6 2 0 2 15 2
## IL5 0 2 15 0 0 9
## IL13 3 0 0 1 21 1
## IL4 2 1 2 3 0 1
## CD14 734 207 449 317 1143 767
## CSNK1A1 12751 4632 8086 5212 11613 6640
## PPARGC1B 646 216 265 274 270 321
## TNIP1 3572 1317 3337 2164 3115 3120
## ATOX1 944 351 715 234 883 475
## FAXDC2 1100 1281 1897 986 442 1296
## IL12B 4 1 1 4 63 1
## MIR146A 4 0 4 1 2 1
## PDLIM7 1169 367 1517 1043 630 1062
## SERPINB1 3181 599 1454 1041 2754 1295
## SSR1 3066 1915 2842 1989 2784 2870
## CD83 475 136 196 216 773 216
## SOX4 1333 1248 2374 1978 1022 1780
## CMAHP 259 349 732 466 310 566
## HLA-A 27869 11994 30218 14566 50156 22146
## HLA-C 36794 13710 15819 10324 55997 23822
## HLA-B 40075 18918 40390 18616 70383 28987
## MICA 402 276 643 348 344 402
## LTA 14 20 22 29 46 19
## TNF 86 39 56 60 203 49
## HLA-DRB1 8538 3353 4656 5866 19692 7281
## HLA-DQB1 959 304 595 3905 10319 3414
## PPARD 2075 588 1207 785 1199 1013
## CCND3 1266 595 1494 963 1747 1349
## VEGFA 1237 671 1352 1109 986 1061
## RUNX2 138 61 156 186 124 196
## IL17A 20 0 0 4 0 0
## IL17F 8 0 0 0 1 0
## PRDM1 1485 285 334 188 911 264
## ATG5 998 478 744 615 1146 741
## TRAF3IP2 1364 644 1373 833 1111 1149
## NCOA7 1680 463 721 626 1985 1044
## SGK1 6110 1686 2741 3394 3440 4323
## IFNGR1 4381 1791 2704 1930 5251 2775
## TNFAIP3 997 465 468 418 1499 611
## SOD2 13755 2822 5586 4480 11205 5790
## LPAL2 12 10 28 10 29 20
## PLG 2 2 3 2 0 0
## CCR6 145 101 108 125 181 120
## RAC1 8221 5163 9291 5149 7923 7016
## ZNF316 1094 724 1691 1341 551 1416
## AHR 3550 1824 3195 2178 3983 3017
## IL6 26 2 4 1 30 3
## TOMM7 4508 3459 7723 3039 3313 3882
## CYCS 5730 1903 3287 2194 4958 3161
## AQP1 9494 4658 15763 8518 7003 12473
## NT5C3A 2163 538 605 350 1260 529
## EGFR 4740 3999 6609 6346 2130 7023
## CD36 4360 1444 2586 1371 2386 3441
## SAMD9 723 131 191 171 582 208
## SERPINE1 240 33 96 166 1099 129
## CUX1 1549 699 1865 1149 1075 1358
## PSMC2 3279 1581 2670 1579 3450 2426
## NAMPT 7998 1283 2396 1900 5149 2254
## HYAL4 214 25 82 55 56 23
## LEP 122 0 379 160 0 576
## CALD1 9548 3907 13284 6368 4055 8550
## BRAF 653 459 691 515 434 688
## EZH2 791 402 583 404 757 448
## DNAJB6 5337 2207 3733 2397 6204 2972
## CSMD1 3 8 29 23 16 54
## CTSB 12824 4960 11403 7090 15838 11175
## EGR3 791 572 478 200 613 362
## TNFRSF10A 298 158 170 142 479 193
## BNIP3L 3281 2711 3238 1767 3344 2515
## DUSP4 679 468 882 895 935 831
## NRG1 193 138 251 426 158 238
## RPL7 57360 53843 80676 38019 42107 52217
## IL7 150 104 205 119 251 155
## GEM 351 136 265 293 1272 274
## MYC 1962 621 1488 1059 1470 1187
## GPT 272 824 431 214 80 246
## JAK2 1065 449 625 445 1194 737
## CD274 231 39 37 39 333 57
## IL33 4734 1400 1895 1031 2980 2312
## LURAP1L-AS1 3 7 5 6 2 13
## LURAP1L 484 223 421 476 1246 537
## TTC39B 1448 1062 675 428 1235 661
## IFNA1 0 0 0 0 0 0
## TEK 615 198 619 384 208 519
## TRBV20OR9-2 0 1 0 0 0 2
## TOMM5 2010 667 1034 470 1307 774
## ANXA1 8480 5083 10080 7663 14230 9482
## ERP44 1481 848 1412 753 1346 1057
## ZNF483 91 96 320 178 69 219
## TNFSF15 66 26 46 64 37 77
## TLR4 457 99 275 133 533 563
## PTGS1 3895 2746 4917 1835 3037 3562
## HSPA5 11847 5770 7906 5068 7936 7342
## FNBP1 2326 1180 2144 1408 1881 1719
## CARD9 203 59 140 156 182 129
## TRAF2 418 339 733 418 610 486
## CLIC3 1341 247 1286 371 733 666
## IL2RA 70 25 21 43 356 56
## GATA3 1982 4126 8294 2604 1194 3715
## VIM 37725 16921 40825 19423 22308 36843
## CREM 479 182 402 248 571 350
## DKK1 27 24 95 55 120 177
## MBL2 0 0 0 0 0 0
## SAR1A 2295 1449 2742 1813 2439 2543
## PRF1 230 32 62 36 241 70
## ZMIZ1 3075 963 2300 2203 1129 2924
## IFIT3 2321 400 739 623 4477 631
## TALDO1 4037 2856 3742 1744 3381 2316
## IGF2 1140 310 851 521 198 628
## INS-IGF2 1073 297 831 486 183 593
## IGF2-AS 1 2 6 0 0 0
## INS 0 0 0 0 0 0
## STIM1 2133 1335 3168 2554 1844 3060
## TRIM22 2684 544 1184 824 4454 1386
## PTH 0 0 0 0 0 0
## SAA1 558 166 89 40 297 71
## SLC1A2 33 49 60 77 52 79
## FOSL1 177 9 11 54 868 27
## CCND1 3724 4168 9545 5754 2075 6347
## JRKL 232 179 406 272 175 300
## MMP7 325 318 2227 1467 648 1323
## MMP1 84 3 8 88 441 14
## MMP3 26 14 39 42 74 125
## CASP1 2155 735 1097 619 2154 998
## DLAT 1057 491 681 714 900 986
## IL18 1999 2046 3503 1024 1775 2122
## CD3E 662 206 151 140 845 161
## MCAM 5876 1605 4526 2957 1595 3543
## WNK1 4629 2058 4058 3133 2561 5004
## TNFRSF1A 3743 1825 5012 2876 3570 3177
## GAPDH 37737 16153 50228 30954 31740 32595
## CD4 1385 435 661 559 1503 960
## SLC2A3 523 222 396 233 966 551
## CLEC4D 5 4 2 0 14 1
## KLRB1 162 49 50 19 94 40
## CD69 206 60 40 26 296 63
## CLEC2B 2881 2017 3357 982 2844 1708
## OLR1 14 2 4 4 10 8
## ABCD2 48 23 41 46 42 89
## LRRK2 471 168 449 276 536 553
## VDR 1348 1169 1148 1259 752 1287
## TMBIM6 13447 10875 14709 10999 11942 13219
## PFDN5 9570 5113 9743 4009 7347 5557
## SP7 0 0 4 2 2 0
## MUCL1 7531 1489 19494 8928 849 8073
## CD63 12574 5589 15407 8681 14079 12806
## SUOX 1818 1436 2720 1646 1477 2022
## RPS26 13935 7524 4174 1936 11556 7536
## RPL41 89027 41784 79753 32959 66860 47160
## IL23A 29 10 24 28 20 14
## DDIT3 438 368 898 502 444 553
## IFNG 24 5 0 0 43 2
## IL22 33 0 0 0 16 0
## LYZ 12362 1130 1867 1435 9536 1960
## CAPS2 52 55 109 105 37 85
## SYT1 32 19 34 29 9 26
## BTG1 6465 5396 9564 4037 8597 7044
## HCAR3 574 281 349 114 793 192
## GJB2 23846 1593 4290 4236 10782 3088
## GJB6 5185 1217 2496 2690 3800 2191
## TNFSF11 19 11 3 1 32 2
## TPT1 169969 73868 117058 47799 126200 83602
## LMO7 1696 627 1549 990 993 1377
## TNFSF13B 435 121 165 80 621 174
## LAMP1 5967 3809 7624 4620 2806 6435
## PSME2 3130 998 1783 1156 4663 1256
## GZMB 438 7 19 6 347 20
## RPL36AL 5431 3762 6764 2829 5062 3459
## PYGL 2398 819 1822 1331 1555 1315
## HIF1A 3110 910 2077 3667 1935 2793
## FOS 683 474 904 1088 5229 1100
## JDP2 702 420 1287 719 1472 1207
## NOXRED1 34 19 44 35 18 33
## SERPINA1 291 38 74 70 443 95
## CDC42BPB 3315 1057 2753 2118 1549 3116
## PLA2G4D 3734 154 292 93 863 162
## TRIM69 36 15 50 27 57 20
## SLC51B 49 16 56 45 24 40
## SMAD3 1507 797 1622 1444 724 1714
## CYP1A1 62 26 69 1 0 1365
## AKAP13 2987 1586 3538 2779 1594 3378
## MEFV 44 5 15 8 18 13
## SOCS1 200 38 127 121 270 163
## ATXN2L 1706 829 1877 1659 1041 1668
## CD19 3 5 7 9 3 3
## ITGAL 712 191 250 161 530 231
## ITGAM 335 117 170 112 327 320
## ITGAX 407 138 239 153 303 197
## DNAJA2 2998 1370 2762 1666 2979 2270
## SIAH1 685 676 1174 760 633 879
## ADCY7 1335 377 742 720 967 1179
## NOD2 749 207 352 211 1061 370
## CMTM2 1 7 12 7 5 9
## SF3B3 3670 1751 3302 2036 1894 2681
## PSMD7 3129 1923 2848 1659 3295 2423
## WWOX 308 181 255 209 213 223
## MAF 3593 3328 5732 3076 4168 4453
## SLC7A5 3753 653 935 553 1092 1231
## CXCL16 1456 485 647 638 1364 923
## XAF1 2060 738 1361 1142 2661 811
## CD68 1023 242 514 278 1409 1066
## PER1 2524 1940 5477 3879 2198 5712
## NOS2 161 4 24 23 5 20
## TRAF4 773 431 1221 1291 962 911
## CCL2 608 329 315 314 698 278
## CCL5 781 327 228 119 821 134
## CCL3 38 1 3 1 73 18
## CCR7 202 54 84 55 438 75
## STAT3 12150 4055 7443 6306 6117 7029
## SOST 472 172 0 29 4 37
## ITGA2B 12 7 30 25 11 15
## EFCAB13 49 84 195 111 32 123
## NPEPPS 2558 1240 2151 1636 2072 2276
## TBX21 35 9 20 8 35 11
## COL1A1 58802 15650 28578 40700 45110 56995
## MRPS23 1464 605 965 647 1293 898
## MIR21 0 0 1 2 0 2
## RPS6KB1 972 450 829 666 968 894
## ACE 882 197 1117 588 401 1084
## ERN1 619 262 665 574 384 632
## PRTN3 1 0 0 0 0 2
## PLIN5 532 1034 165 72 84 119
## RETN 0 0 4 1 6 0
## CCL25 4 1 1 0 0 0
## ICAM1 706 281 608 329 1235 668
## TYK2 1484 727 1703 1595 1102 1750
## SMARCA4 3611 1863 4145 2980 1342 2589
## ACP5 1413 530 498 295 1633 543
## JUNB 4083 1307 3388 2177 7000 2491
## CYP4F22 1874 514 804 87 631 368
## JAK3 487 114 195 165 348 179
## JUND 2479 1554 3331 2235 4508 3261
## CEBPA 7062 3890 6238 2321 3870 4251
## CEBPG 2079 1334 2275 1429 2418 1894
## ACP7 1567 88 127 20 469 57
## ZFP36 1932 643 1280 914 2274 1291
## TGFB1 1768 528 1243 1001 1686 988
## RPS19 35572 22433 46604 17366 30996 26031
## PSG2 0 1 1 0 0 2
## FUT2 374 71 325 155 188 181
## NKG7 221 21 51 20 204 51
## ZNF415 105 84 259 190 130 274
## LILRB2 265 38 72 51 178 95
## LILRA5 54 2 3 0 24 4
## KIR3DL1 0 0 2 0 0 0
## KIR3DL2 11 8 0 0 3 3
## SMOX 900 89 288 161 373 151
## BMP2 279 287 592 315 467 365
## GINS1 326 118 152 126 327 133
## PLCG1 2089 1124 2516 1909 1270 2110
## YWHAB 10093 7740 11220 5621 8925 8748
## PI3 31002 93 104 5 1682 75
## MMP9 260 50 44 96 764 287
## CD40 659 345 812 427 1077 507
## PFDN4 840 460 834 330 752 436
## BMP7 772 482 802 426 204 705
## RPS21 25233 10677 20028 7402 14336 10102
## MX1 3111 390 774 1088 2491 634
## PFKL 3770 1836 4874 3150 3237 4106
## IL17RA 927 500 1067 612 585 833
## UBE2L3 3000 1479 2648 1540 2675 2140
## TPST2 1006 426 979 535 959 857
## XBP1 5474 2163 4945 5453 3873 5117
## UQCR10 1801 1026 1437 756 1470 1059
## SEC14L2 373 172 400 430 314 409
## APOL6 3250 602 853 762 3301 894
## APOL1 1778 312 594 392 1600 491
## TYMP 10214 823 1029 1155 16254 1448
## BEND2 1 1 0 0 0 1
## PHEX 35 6 13 34 48 17
## TIMP1 1764 576 1751 930 1555 1659
## FOXP3 157 57 67 57 403 92
## MSN 9985 2642 5432 4385 8631 7037
## PGK1 11424 3784 7745 6380 13301 7879
## LAMP2 4464 3129 4978 4023 4737 4664
## CD40LG 107 68 22 21 60 25
## IRAK1 3129 1061 1801 1558 2539 2400
## COX2 55590 45347 119184 32088 11093 43778
## GSM5629977 GSM5629978 GSM5629979 GSM5629980 GSM5629981 GSM5629982
## TNFRSF9 67 52 7 50 9 18
## ENO1 18134 32086 18427 23845 14243 18646
## PIK3CD 303 643 455 270 230 680
## PGD 4110 7442 1541 2889 5913 2652
## MTHFR 606 1180 831 735 591 1142
## TNFRSF1B 916 1270 751 804 597 1110
## PINK1 1662 4601 1914 1224 2573 2530
## IFNLR1 189 572 397 343 211 321
## RUNX3 943 1793 1365 378 528 1338
## SH3BGRL3 2773 4811 2450 5284 3531 3813
## CD52 422 421 227 573 278 508
## IFI6 1237 926 761 10302 518 1356
## ZC3H12A 1528 1909 724 5536 1172 712
## UTP11 687 1060 689 1724 663 849
## JUN 1532 2478 1221 1198 1469 2200
## KANK4 81 292 71 6 126 114
## EFCAB7 215 367 242 203 227 271
## IL23R 5 0 4 7 0 8
## ADGRL2 655 1621 710 1078 1167 973
## GBP3 613 899 595 1074 265 626
## GBP1 980 806 481 3755 570 589
## GBP5 242 209 106 727 107 240
## TGFBR3 1764 3767 2660 933 1819 3689
## VCAM1 391 537 261 507 402 598
## PTPN22 155 160 71 165 64 128
## CD160 17 39 20 19 22 30
## FCGR1A 23 18 11 175 22 23
## MCL1 3121 6672 4572 6921 4070 6050
## CTSK 5212 11733 14290 18078 9699 25688
## RORC 701 2363 564 131 760 895
## S100A9 21852 4827 189 655111 2408 638
## S100A12 9 0 1 4757 6 25
## S100A8 32680 6350 203 552503 2938 370
## IL6R 543 995 659 646 423 993
## RIT1 387 931 502 1046 458 893
## BGLAP 19 34 37 17 12 38
## IFI16 2578 3259 2438 4642 1550 3075
## AIM2 20 33 12 137 25 39
## CRP 0 1 0 0 0 0
## FCGR2A 151 250 172 640 149 447
## HSPA6 110 182 159 386 96 222
## FCGR3A 117 70 39 627 79 145
## SELL 78 82 64 276 53 203
## GLUL 10003 17913 9185 26193 8478 16307
## PTGS2 26 71 24 352 18 49
## CRB1 6 14 6 0 0 5
## KDM5B 1951 4134 1892 1320 1619 1876
## IL10 14 8 6 45 6 15
## YOD1 287 681 438 3475 606 1045
## HHAT 187 361 169 101 147 204
## TRAF5 295 594 494 264 279 762
## NLRP3 39 65 40 121 32 99
## LINC01250 0 2 0 0 1 0
## RPS7 26885 39359 20753 49551 25418 26466
## RSAD2 180 112 111 2313 84 187
## FOSL2 2209 6371 3344 3263 3694 3848
## REL 1029 1616 888 1416 897 1135
## TGFA 557 1339 357 3876 1056 782
## DYSF 146 422 308 325 342 494
## HK2 881 2510 603 2220 2236 843
## CD8A 193 226 125 181 86 190
## CD8B 69 104 74 136 69 129
## EIF5B 4482 6775 4322 2949 3617 4790
## IL1R1 1466 3153 2520 2065 1618 3108
## RGPD6 452 969 723 251 376 628
## IL1A 25 78 21 103 95 42
## IL1B 30 85 7 1039 61 21
## IL37 49 402 359 316 346 960
## IL36RN 163 564 514 27362 502 1301
## IL1F10 8 23 17 140 17 93
## IL1RN 845 2042 1573 4388 1095 1395
## NMI 683 735 557 2039 508 621
## TNFAIP6 57 107 92 457 143 150
## IFIH1 695 751 483 1386 347 668
## SCN1A 8 25 8 0 15 10
## ABCB11 104 254 34 19 139 27
## RBM45 153 299 227 222 153 228
## FRZB 610 2339 1859 288 321 1629
## TFPI 432 753 532 572 468 1165
## STAT1 2847 3380 1714 7997 1729 2423
## NABP1 305 539 287 867 275 526
## SF3B1 6203 12002 8625 6659 6219 11071
## CASP10 356 585 368 559 254 526
## CD28 91 95 46 135 40 126
## CTLA4 82 71 19 60 16 42
## ICOS 56 59 15 85 19 36
## NDUFS1 3112 6205 2450 2990 4185 3012
## CXCR2 91 140 64 495 121 162
## IRS1 386 1069 526 180 618 1439
## CCL20 24 11 8 101 10 5
## ATG16L1 632 1481 805 711 608 1192
## GPR35 41 97 63 25 20 73
## PDCD1 30 42 12 17 17 23
## PPARG 886 1404 208 182 1235 903
## RPL15 30127 57600 35584 53060 35342 48378
## EOMES 32 44 26 28 26 26
## CX3CR1 136 238 121 135 159 210
## ACKR2 20 56 25 354 56 54
## CCR2 239 387 176 340 234 410
## CCRL2 24 15 15 166 22 47
## TLR9 19 22 25 17 16 54
## ARHGEF3 554 1191 854 1172 622 1076
## ADAMTS9 128 294 243 120 164 239
## TMEM45A 8411 17313 2286 20537 15127 6663
## CD80 12 6 2 53 3 7
## CD86 159 218 136 436 115 244
## MIX23 285 352 227 451 220 279
## PLS1 84 200 208 172 75 182
## PTX3 26 68 44 205 58 196
## GOLIM4 627 1760 1627 1000 840 2445
## MYNN 493 942 600 1126 525 960
## TNFSF10 2760 3297 2390 6594 1570 3454
## ADIPOQ 146 153 715 314 585 5122
## SPON2 353 1112 1384 737 590 1760
## S100P 300 626 1152 3723 243 427
## WDR1 3766 9181 6192 7724 4868 6716
## CD38 6 7 10 38 9 21
## PPARGC1A 495 1396 981 166 553 1049
## TMPRSS11B 0 0 0 0 0 0
## CSN3 0 0 4 0 0 0
## ALB 6 13 4 0 9 2
## CXCL8 14 5 0 22721 8 3
## CXCL2 27 13 9 317 5 10
## AREG 103 128 76 434 172 112
## CXCL10 947 157 3 494 132 18
## CXCL13 59 37 1 161 4 7
## SPP1 136 106 30 79 11 1143
## HERC6 397 365 261 1560 167 314
## NFKB1 1158 2288 1568 1209 933 1699
## SEC24B 910 1715 1108 638 879 1264
## EGF 82 317 119 11 65 90
## IL2 4 1 2 1 0 0
## IL21 6 2 0 5 0 0
## IL21-AS1 2 1 0 1 0 0
## SLC7A11 33 36 21 232 26 41
## IL15 104 157 96 178 91 99
## EDNRA 159 499 509 308 200 521
## TLR2 392 506 217 416 346 341
## FGB 0 0 0 0 0 0
## DDX60 497 595 492 1879 352 886
## SPCS3 1810 3384 1816 3888 2044 2625
## TLR3 130 173 106 153 118 259
## OSMR 478 901 673 1069 607 937
## GZMK 64 93 22 160 69 54
## GZMA 119 140 25 335 98 68
## ANKRD55 3 14 6 11 14 23
## CENPK 143 163 159 417 89 103
## CAST 4867 10982 8420 8646 6743 11751
## ERAP1 1706 3601 2620 1789 2046 3366
## ERAP2 256 290 1048 178 161 317
## TNFAIP8 873 1281 969 1657 764 1287
## CSF2 5 12 5 28 1 12
## IL5 1 4 0 0 2 2
## IL13 6 10 2 5 1 4
## IL4 3 4 0 1 3 0
## CD14 504 968 455 2008 696 1738
## CSNK1A1 5016 9795 6580 13348 5522 7571
## PPARGC1B 518 835 340 370 457 401
## TNIP1 1901 4327 2448 4322 2239 3582
## ATOX1 562 973 481 1083 629 693
## FAXDC2 1892 5420 1392 570 3880 2366
## IL12B 15 4 4 62 0 4
## MIR146A 2 4 1 1 1 5
## PDLIM7 659 1206 1249 823 685 1756
## SERPINB1 1312 1675 1327 12365 942 1478
## SSR1 2364 4405 2375 2845 2233 3561
## CD83 344 326 146 520 147 317
## SOX4 1367 2417 1229 544 866 1525
## CMAHP 341 661 603 353 446 1250
## HLA-A 13250 23160 19255 26580 14280 25820
## HLA-C 18552 35094 10326 24547 13546 26260
## HLA-B 24886 37872 25595 42980 19577 32053
## MICA 362 704 697 492 363 619
## LTA 26 53 27 24 18 51
## TNF 98 118 66 132 60 104
## HLA-DRB1 4092 5805 3003 14947 6544 8924
## HLA-DQB1 513 856 605 11875 4929 2572
## PPARD 774 1956 900 2294 1114 1273
## CCND3 1039 2265 1337 1238 911 1629
## VEGFA 1347 2804 1185 1681 1590 1540
## RUNX2 92 218 191 84 99 178
## IL17A 0 0 0 59 0 0
## IL17F 0 0 0 42 0 0
## PRDM1 318 606 214 1536 644 530
## ATG5 723 1167 801 1335 787 1100
## TRAF3IP2 722 1431 1027 899 741 1119
## NCOA7 870 1277 739 1837 732 1385
## SGK1 2073 3468 2689 4575 1708 4118
## IFNGR1 3006 4451 2797 8653 2799 3750
## TNFAIP3 585 952 543 620 511 713
## SOD2 7764 11102 5321 20288 5060 6491
## LPAL2 12 18 15 6 13 21
## PLG 2 5 1 0 3 2
## CCR6 134 232 123 127 100 154
## RAC1 5726 9682 6373 7028 5316 6685
## ZNF316 881 2525 1615 706 954 1729
## AHR 2696 4246 2804 2568 2461 3772
## IL6 2 1 2 15 2 4
## TOMM7 3020 5609 3313 4013 3814 4974
## CYCS 3012 5071 3054 9926 3311 3393
## AQP1 4975 12390 12787 7040 6523 13183
## NT5C3A 746 1250 511 7276 897 734
## EGFR 3492 8397 6478 1649 4307 6677
## CD36 2986 5298 1417 6979 5307 5492
## SAMD9 177 303 192 1793 141 291
## SERPINE1 101 83 66 242 59 76
## CUX1 627 1500 1369 1115 853 1593
## PSMC2 2411 4154 2231 4866 2913 2886
## NAMPT 2955 4578 2050 20049 3093 3184
## HYAL4 36 28 71 523 17 66
## LEP 27 36 154 124 275 576
## CALD1 3979 8197 10227 4417 4403 11298
## BRAF 436 1175 656 414 538 949
## EZH2 653 996 544 1034 444 719
## DNAJB6 2695 5952 2980 10732 3896 4072
## CSMD1 22 49 99 9 22 29
## CTSB 9408 16508 9291 19336 10703 15667
## EGR3 1180 2751 301 905 1887 1228
## TNFRSF10A 195 355 218 329 154 165
## BNIP3L 4040 8371 2177 8603 6987 4460
## DUSP4 1380 2290 424 373 1313 937
## NRG1 87 199 253 127 115 274
## RPL7 49602 91984 57569 63039 56871 66063
## IL7 103 191 172 188 134 271
## GEM 125 288 259 370 219 411
## MYC 1261 1867 1028 1195 928 1460
## GPT 1261 3451 317 195 2452 858
## JAK2 559 1167 755 769 554 1103
## CD274 98 81 60 466 44 103
## IL33 567 1161 2129 3659 1549 2626
## LURAP1L-AS1 3 4 2 0 3 3
## LURAP1L 257 571 390 540 286 648
## TTC39B 2406 4520 693 2875 3453 1428
## IFNA1 0 0 0 0 0 0
## TEK 180 414 419 153 203 495
## TRBV20OR9-2 0 0 0 0 0 0
## TOMM5 960 1468 818 2617 1188 1143
## ANXA1 5530 10183 8906 38829 6888 13711
## ERP44 1011 1757 1088 1424 1077 1320
## ZNF483 140 343 301 68 112 313
## TNFSF15 55 107 51 60 31 60
## TLR4 155 401 322 737 314 1249
## PTGS1 1832 4893 3730 2196 1804 4908
## HSPA5 8605 14740 6235 8734 9005 7695
## FNBP1 1379 2437 1911 1469 1275 2571
## CARD9 99 253 155 177 115 176
## TRAF2 447 886 541 322 384 527
## CLIC3 196 922 760 1688 563 1231
## IL2RA 109 101 49 87 21 98
## GATA3 2635 5137 3244 465 2053 3424
## VIM 16955 35374 29800 20240 22729 55494
## CREM 266 559 312 653 302 423
## DKK1 15 62 101 52 26 82
## MBL2 0 0 0 0 0 1
## SAR1A 1877 3720 2129 2787 1963 3070
## PRF1 76 108 61 105 61 125
## ZMIZ1 1272 3684 2654 1225 1789 3286
## IFIT3 742 589 500 4173 381 828
## TALDO1 4526 8402 2397 5288 6594 3757
## IGF2 149 394 369 228 269 386
## INS-IGF2 143 378 356 214 258 377
## IGF2-AS 1 1 3 1 0 0
## INS 0 0 0 0 0 0
## STIM1 1292 3567 2866 1369 1545 3193
## TRIM22 1336 1807 1143 3486 727 2217
## PTH 0 0 0 0 0 0
## SAA1 4109 1739 92 609 1141 382
## SLC1A2 70 207 139 34 63 204
## FOSL1 20 37 19 139 18 28
## CCND1 2850 7742 6555 1216 3941 5938
## JRKL 202 450 378 133 201 413
## MMP7 1834 1883 1353 1235 339 1690
## MMP1 42 2 4 2152 13 6
## MMP3 11 15 75 203 2 36
## CASP1 837 1397 852 2937 929 1312
## DLAT 775 1604 949 1053 1065 1050
## IL18 1263 2108 1964 2231 1375 2515
## CD3E 302 400 183 259 192 296
## MCAM 2452 5166 3172 2032 2708 4264
## WNK1 2380 6630 4028 1976 3317 5706
## TNFRSF1A 2638 5284 3466 3265 2618 4253
## GAPDH 23044 47941 33788 41884 17720 32713
## CD4 683 1172 756 904 699 1793
## SLC2A3 380 471 307 936 220 720
## CLEC4D 2 3 0 32 1 2
## KLRB1 25 32 34 141 44 67
## CD69 72 81 36 207 48 61
## CLEC2B 1464 1573 2141 2724 884 2146
## OLR1 12 19 3 19 8 3
## ABCD2 32 32 41 29 31 137
## LRRK2 301 613 505 379 286 653
## VDR 1840 3423 1077 787 1555 1133
## TMBIM6 18914 38690 12902 15301 24708 17962
## PFDN5 5908 9495 5903 11452 6472 8232
## SP7 0 2 3 0 2 1
## MUCL1 5535 18893 14013 1531 1942 10979
## CD63 8567 15285 12114 19256 10609 20195
## SUOX 1761 4203 1997 1170 2350 2507
## RPS26 5325 7246 8266 17915 11715 4053
## RPL41 61242 92147 56791 110931 67963 73395
## IL23A 13 22 17 63 10 18
## DDIT3 393 779 487 585 390 743
## IFNG 5 4 0 40 6 0
## IL22 4 2 0 67 0 0
## LYZ 3367 2675 2784 9558 2422 5210
## CAPS2 69 185 116 48 57 146
## SYT1 9 32 31 10 7 67
## BTG1 5870 10780 5619 6184 4532 5922
## HCAR3 190 327 210 2323 136 294
## GJB2 4122 5090 2240 40229 3087 1923
## GJB6 2184 2885 2069 17704 1652 2003
## TNFSF11 43 64 13 2 27 15
## TPT1 83080 132476 86688 266201 98731 113773
## LMO7 527 1545 958 2281 681 1469
## TNFSF13B 233 223 138 547 194 336
## LAMP1 4414 11081 5262 3058 5708 6386
## PSME2 1871 2229 1308 3667 1375 1610
## GZMB 34 17 7 422 28 37
## RPL36AL 4313 6147 3566 5675 3687 4391
## PYGL 950 1560 1360 2319 983 1687
## HIF1A 1191 2884 2433 2987 1224 2345
## FOS 949 738 510 328 278 759
## JDP2 352 944 942 703 785 1775
## NOXRED1 37 50 51 20 22 44
## SERPINA1 154 251 47 879 86 141
## CDC42BPB 1329 3790 2614 1691 1928 3673
## PLA2G4D 320 559 199 5852 407 285
## TRIM69 29 55 38 47 13 36
## SLC51B 30 37 43 19 19 61
## SMAD3 907 2232 1317 656 1073 1727
## CYP1A1 6 51 2326 1 1 19
## AKAP13 1756 4338 2828 1337 2146 4202
## MEFV 7 23 15 91 16 44
## SOCS1 114 219 112 251 135 121
## ATXN2L 1106 2632 1909 903 1199 2530
## CD19 4 9 9 9 10 9
## ITGAL 351 471 227 232 188 374
## ITGAM 188 371 255 386 248 679
## ITGAX 306 447 250 796 179 676
## DNAJA2 1842 3561 2183 4598 1970 2924
## SIAH1 803 1414 984 686 680 923
## ADCY7 729 1637 1220 918 814 2372
## NOD2 489 766 344 1848 402 402
## CMTM2 9 14 10 19 8 16
## SF3B3 2334 4443 2586 2421 3086 3044
## PSMD7 2860 4873 2240 4195 3454 2844
## WWOX 205 385 235 282 231 296
## MAF 2402 5255 3937 1964 2560 5008
## SLC7A5 2224 4208 1507 1689 2253 926
## CXCL16 708 1202 697 904 640 882
## XAF1 1181 1086 981 1595 573 1306
## CD68 546 806 667 1774 615 2184
## PER1 1241 3652 4316 1801 3178 3912
## NOS2 3 16 7 486 11 39
## TRAF4 921 2325 1043 994 886 1205
## CCL2 421 318 263 487 288 580
## CCL5 300 365 84 284 203 289
## CCL3 7 3 3 518 7 19
## CCR7 194 100 66 192 36 188
## STAT3 5813 11756 5525 12651 5312 6661
## SOST 0 3 2 372 2 2
## ITGA2B 12 36 18 16 17 57
## EFCAB13 110 182 167 35 53 167
## NPEPPS 1882 3912 2368 3252 2142 2996
## TBX21 5 24 12 13 21 15
## COL1A1 16216 51840 42677 26712 112739 162513
## MRPS23 875 1405 821 2228 948 1125
## MIR21 2 2 3 4 2 3
## RPS6KB1 674 1343 843 827 791 1078
## ACE 218 570 651 273 485 940
## ERN1 369 1031 592 507 434 753
## PRTN3 1 0 0 1 1 0
## PLIN5 2042 5181 95 969 4085 733
## RETN 1 4 2 8 0 6
## CCL25 1 2 0 0 0 0
## ICAM1 496 660 511 861 445 920
## TYK2 1205 2982 1831 1153 1228 2619
## SMARCA4 1569 3899 2492 1285 1932 2674
## ACP5 1284 2732 413 2540 1579 1609
## JUNB 2847 4512 2589 7241 2599 2949
## CYP4F22 988 3131 311 2125 2392 970
## JAK3 256 334 163 299 143 349
## JUND 1470 3723 1990 1355 2332 2621
## CEBPA 4812 10002 3832 5920 6753 3869
## CEBPG 1267 2719 1828 2532 1461 1844
## ACP7 128 289 51 5329 533 273
## ZFP36 820 1415 1035 2324 838 1564
## TGFB1 623 1393 940 909 721 1754
## RPS19 23732 42429 26236 42132 24217 31118
## PSG2 2 2 4 5 2 1
## FUT2 120 258 165 359 55 249
## NKG7 69 108 36 156 62 83
## ZNF415 197 474 254 76 133 178
## LILRB2 109 144 89 320 131 154
## LILRA5 15 8 4 138 6 24
## KIR3DL1 0 0 1 0 2 0
## KIR3DL2 0 2 0 1 1 2
## SMOX 112 452 151 1684 462 316
## BMP2 145 387 362 376 183 233
## GINS1 238 295 146 497 149 197
## PLCG1 1808 3650 2116 1859 1604 2765
## YWHAB 7790 13456 7848 7209 7778 9259
## PI3 224 54 48 57934 122 89
## MMP9 154 158 77 248 145 291
## CD40 455 605 452 435 375 575
## PFDN4 606 692 431 1124 468 568
## BMP7 444 1338 725 564 696 948
## RPS21 14029 18458 12505 26388 13560 14433
## MX1 878 808 697 2882 282 765
## PFKL 3720 9871 4103 2634 5096 5278
## IL17RA 844 1647 877 603 815 1176
## UBE2L3 2089 3614 1812 2973 2450 2332
## TPST2 459 1043 638 795 609 998
## XBP1 4130 10937 4924 6520 4610 5375
## UQCR10 1487 2618 1076 2228 1843 1580
## SEC14L2 532 1135 301 474 406 336
## APOL6 1121 1154 750 2571 619 1623
## APOL1 381 365 301 1354 369 422
## TYMP 2537 3148 1091 13928 2148 2093
## BEND2 0 0 2 0 1 1
## PHEX 15 44 18 74 15 38
## TIMP1 903 1498 1380 1664 1242 2607
## FOXP3 119 222 71 106 82 159
## MSN 3554 7083 5438 6692 4117 8264
## PGK1 8286 14504 7656 22293 8328 9929
## LAMP2 4487 8475 4168 5227 4912 5611
## CD40LG 62 81 50 23 26 56
## IRAK1 1957 4638 2328 2897 2267 2926
## COX2 31907 69863 38355 13776 44114 45829
## GSM5629983 GSM5629984 GSM5629985 GSM5629986 GSM5629987 GSM5629988
## TNFRSF9 102 12 23 51 51 15
## ENO1 29875 15939 11718 18625 23955 13852
## PIK3CD 440 292 399 147 552 252
## PGD 3555 5560 1682 2576 3945 2986
## MTHFR 878 686 688 371 1273 474
## TNFRSF1B 1460 604 809 615 1176 590
## PINK1 1207 2614 1603 1130 2821 1337
## IFNLR1 240 135 290 105 504 135
## RUNX3 958 498 1198 470 1929 410
## SH3BGRL3 5313 3056 2817 3273 4032 2894
## CD52 498 167 212 604 230 269
## IFI6 6549 324 919 4446 1152 517
## ZC3H12A 6893 1955 824 1567 1096 773
## UTP11 1205 487 584 1188 755 563
## JUN 1334 1752 2314 947 2566 1413
## KANK4 11 176 65 51 100 106
## EFCAB7 145 137 138 197 261 139
## IL23R 10 7 5 7 0 5
## ADGRL2 1015 1675 830 484 1459 1015
## GBP3 838 170 329 918 735 445
## GBP1 5428 504 596 1512 717 528
## GBP5 1145 60 115 170 123 120
## TGFBR3 1231 1907 2654 1465 3827 1518
## VCAM1 605 398 340 778 516 439
## PTPN22 153 78 99 81 125 83
## CD160 22 8 21 15 23 10
## FCGR1A 209 12 12 85 16 10
## MCL1 7613 4217 6975 4072 7331 4088
## CTSK 11015 5874 15375 16726 16061 13147
## RORC 157 1166 574 118 1170 580
## S100A9 259724 9444 2632 276402 481 11129
## S100A12 1571 18 3 913 2 7
## S100A8 230363 7009 2061 325460 362 13818
## IL6R 691 541 914 425 1050 481
## RIT1 706 465 535 678 792 532
## BGLAP 15 16 16 10 42 13
## IFI16 7926 1386 2256 3305 3243 2026
## AIM2 351 13 20 92 31 10
## CRP 0 0 1 1 0 0
## FCGR2A 1777 166 272 168 219 157
## HSPA6 420 72 171 156 151 63
## FCGR3A 631 48 76 304 34 47
## SELL 420 59 82 157 106 97
## GLUL 13425 9292 9001 13735 14400 9796
## PTGS2 1303 16 62 43 51 21
## CRB1 5 7 3 2 9 6
## KDM5B 2016 1935 1596 984 2957 1717
## IL10 37 8 11 12 2 6
## YOD1 2554 737 1189 768 700 357
## HHAT 164 184 172 70 250 187
## TRAF5 295 215 399 238 424 238
## NLRP3 270 44 116 26 67 36
## LINC01250 0 0 2 2 0 0
## RPS7 23150 14016 12150 48820 21706 19163
## RSAD2 995 73 73 248 202 57
## FOSL2 5228 4684 5095 1216 7354 3086
## REL 1187 1005 1248 884 1668 798
## TGFA 2214 1150 610 690 714 551
## DYSF 415 271 309 232 735 185
## HK2 3685 3163 761 693 1737 1242
## CD8A 766 148 157 126 109 63
## CD8B 321 84 79 89 49 33
## EIF5B 4315 2746 3581 3863 6022 3249
## IL1R1 2645 1644 2251 1473 2893 1591
## RGPD6 306 345 550 246 790 287
## IL1A 407 107 26 14 42 67
## IL1B 5413 132 20 40 29 94
## IL37 159 352 841 49 480 213
## IL36RN 9822 732 1422 6378 942 454
## IL1F10 62 41 67 31 38 24
## IL1RN 7223 1069 1132 1263 1828 1123
## NMI 1655 346 327 1401 538 434
## TNFAIP6 387 55 244 118 124 119
## IFIH1 1381 271 518 782 692 371
## SCN1A 0 1 8 0 19 2
## ABCB11 9 115 20 3 66 44
## RBM45 214 119 154 176 234 141
## FRZB 380 359 1248 570 1859 602
## TFPI 476 317 615 817 820 465
## STAT1 11179 1531 2117 5234 2995 1756
## NABP1 688 273 237 274 290 238
## SF3B1 6760 5454 7048 4188 10427 4657
## CASP10 605 204 333 256 455 219
## CD28 189 101 104 86 68 72
## CTLA4 149 32 36 52 46 27
## ICOS 126 16 17 83 42 30
## NDUFS1 2637 4673 2062 2053 4232 2371
## CXCR2 531 122 106 244 117 169
## IRS1 415 788 902 273 1456 394
## CCL20 57 20 33 282 26 36
## ATG16L1 616 613 708 446 1078 403
## GPR35 23 35 53 18 71 16
## PDCD1 55 20 12 26 18 18
## PPARG 195 997 147 635 604 722
## RPL15 30460 23149 23114 53888 42002 30016
## EOMES 68 18 17 25 40 19
## CX3CR1 85 124 142 125 248 210
## ACKR2 302 18 29 145 31 28
## CCR2 241 150 192 267 280 315
## CCRL2 169 18 13 27 23 14
## TLR9 8 18 16 3 32 10
## ARHGEF3 1154 525 807 743 1098 649
## ADAMTS9 177 175 270 81 391 170
## TMEM45A 11710 19115 6604 14374 8012 14504
## CD80 54 5 12 15 7 1
## CD86 312 102 171 203 201 125
## MIX23 332 164 151 504 250 171
## PLS1 139 70 141 124 256 86
## PTX3 198 18 61 127 120 38
## GOLIM4 1123 1018 1552 810 2618 881
## MYNN 684 465 577 738 749 391
## TNFSF10 4218 846 1194 4715 2502 1818
## ADIPOQ 70 695 478 4110 1992 1231
## SPON2 706 414 1334 607 1954 384
## S100P 568 153 281 492 643 81
## WDR1 9237 6542 5799 4406 7524 4164
## CD38 43 7 13 31 20 9
## PPARGC1A 130 691 512 121 1501 254
## TMPRSS11B 0 0 0 0 0 0
## CSN3 0 0 0 0 0 0
## ALB 0 7 11 6 5 4
## CXCL8 27702 41 8 247 3 2
## CXCL2 304 9 18 44 7 3
## AREG 253 174 102 157 193 137
## CXCL10 2359 44 16 179 18 10
## CXCL13 493 20 18 451 4 42
## SPP1 239 7 56 22 59 100
## HERC6 1992 144 204 588 395 173
## NFKB1 2162 1191 1892 1216 2356 1013
## SEC24B 806 843 1032 730 1566 689
## EGF 13 44 100 26 167 19
## IL2 2 0 1 5 3 1
## IL21 17 1 0 1 0 0
## IL21-AS1 3 1 0 4 0 1
## SLC7A11 353 77 55 110 39 26
## IL15 149 43 93 166 130 95
## EDNRA 547 299 532 414 758 406
## TLR2 551 436 155 224 434 340
## FGB 0 0 0 0 0 0
## DDX60 1819 422 745 893 859 492
## SPCS3 3246 2166 1935 3235 2571 1748
## TLR3 149 63 131 169 171 147
## OSMR 1433 835 895 529 990 554
## GZMK 219 36 53 65 56 59
## GZMA 340 39 63 322 99 35
## ANKRD55 3 11 8 17 16 14
## CENPK 274 81 78 203 81 67
## CAST 7781 6829 9079 4833 11216 5493
## ERAP1 2143 1874 2588 1597 3397 1961
## ERAP2 2531 937 147 117 195 95
## TNFAIP8 1264 556 632 1485 1121 835
## CSF2 14 7 6 7 1 2
## IL5 2 2 2 0 5 0
## IL13 4 0 0 0 0 0
## IL4 0 0 5 0 2 0
## CD14 1876 701 823 827 860 368
## CSNK1A1 13238 5717 6868 8550 8464 5505
## PPARGC1B 413 480 333 245 588 385
## TNIP1 4364 2759 2389 2466 3785 1915
## ATOX1 769 419 388 1122 566 416
## FAXDC2 760 6749 1036 567 3008 2706
## IL12B 20 1 1 19 13 3
## MIR146A 1 0 1 4 3 1
## PDLIM7 981 735 993 725 1330 411
## SERPINB1 9427 764 1277 2460 1377 861
## SSR1 3619 2804 2548 2448 3801 2797
## CD83 442 177 278 401 445 222
## SOX4 1197 983 1194 546 2142 1181
## CMAHP 346 247 570 171 692 251
## HLA-A 21472 8592 17214 30711 27034 12763
## HLA-C 30502 11645 18638 31542 26325 12678
## HLA-B 46775 14887 22348 41564 35103 21407
## MICA 324 255 291 413 707 335
## LTA 13 11 42 8 48 15
## TNF 169 45 111 37 59 31
## HLA-DRB1 12776 4899 4558 8231 6259 6209
## HLA-DQB1 9541 4070 934 732 887 1275
## PPARD 2722 1734 1177 766 1516 672
## CCND3 1376 999 1067 998 1480 778
## VEGFA 1387 1405 696 379 1868 850
## RUNX2 114 85 177 35 232 104
## IL17A 80 2 0 8 0 0
## IL17F 63 2 1 54 0 1
## PRDM1 1616 953 618 757 657 430
## ATG5 889 500 555 1155 879 607
## TRAF3IP2 1334 760 763 674 1361 799
## NCOA7 2837 722 1030 969 1162 674
## SGK1 5559 1499 2724 3079 5045 1608
## IFNGR1 5913 2242 2342 5119 3379 2819
## TNFAIP3 1828 577 1021 477 979 428
## SOD2 27605 5241 5199 11682 7111 4692
## LPAL2 11 7 15 2 8 6
## PLG 2 0 2 1 5 2
## CCR6 108 121 223 57 145 188
## RAC1 8093 4614 5776 7442 8201 5352
## ZNF316 771 1098 1494 400 2196 620
## AHR 3423 2485 3369 3183 4491 2520
## IL6 158 3 3 6 8 3
## TOMM7 3158 2504 2413 6554 4441 2934
## CYCS 6944 3356 2244 7144 3743 2831
## AQP1 9838 5117 10542 8695 15669 5998
## NT5C3A 2289 767 586 2379 769 654
## EGFR 2778 4949 6088 1287 10687 3320
## CD36 3440 4565 1334 8367 2747 2794
## SAMD9 1599 164 251 256 288 262
## SERPINE1 526 135 424 67 93 142
## CUX1 1330 851 1394 754 1962 710
## PSMC2 3521 2371 2230 3895 2910 2233
## NAMPT 19581 2699 3363 6644 3079 2363
## HYAL4 135 14 26 155 32 11
## LEP 2 108 89 1750 280 186
## CALD1 5397 4935 7804 7963 10602 4556
## BRAF 488 581 738 352 1128 503
## EZH2 818 392 453 603 691 323
## DNAJB6 7538 3632 3128 4681 3889 2740
## CSMD1 0 33 60 17 61 23
## CTSB 23498 17619 11564 11683 14342 11414
## EGR3 729 2897 1589 399 1836 1055
## TNFRSF10A 414 139 199 140 254 115
## BNIP3L 4459 6624 2840 4380 4441 4270
## DUSP4 655 1289 805 646 1587 553
## NRG1 343 136 289 49 373 137
## RPL7 44224 36426 33251 75682 64897 53076
## IL7 161 95 150 220 226 130
## GEM 446 173 385 455 399 235
## MYC 2319 843 1543 1095 1941 807
## GPT 102 3249 325 120 1622 964
## JAK2 815 476 842 635 1037 606
## CD274 882 50 70 161 65 35
## IL33 1851 1047 1433 2177 2197 1229
## LURAP1L-AS1 3 3 6 3 5 3
## LURAP1L 373 212 366 581 525 285
## TTC39B 1461 3623 961 995 1724 1789
## IFNA1 0 0 0 0 0 0
## TEK 297 179 344 261 537 203
## TRBV20OR9-2 0 0 0 0 0 0
## TOMM5 1456 759 669 2386 828 804
## ANXA1 36500 5300 10320 9918 11783 6736
## ERP44 1529 838 905 1358 1322 1021
## ZNF483 85 96 170 85 250 108
## TNFSF15 38 40 21 45 101 41
## TLR4 902 280 459 453 410 307
## PTGS1 2555 1661 4785 1694 4883 1682
## HSPA5 11937 10493 6257 7603 10016 7490
## FNBP1 1872 1327 2059 1247 2946 1047
## CARD9 116 152 177 63 173 78
## TRAF2 308 385 345 259 669 289
## CLIC3 913 560 839 805 954 427
## IL2RA 202 67 120 57 66 57
## GATA3 720 1588 3121 931 4767 1746
## VIM 23387 16357 34431 36769 44806 22200
## CREM 595 302 314 522 412 227
## DKK1 12 10 86 24 138 15
## MBL2 0 0 0 0 0 0
## SAR1A 2460 1701 2254 2864 3463 1911
## PRF1 407 52 68 137 113 42
## ZMIZ1 1963 2439 2676 741 4311 1360
## IFIT3 3875 231 430 1467 700 360
## TALDO1 4141 4894 1880 3961 3939 3560
## IGF2 342 139 339 579 641 145
## INS-IGF2 318 126 309 567 592 137
## IGF2-AS 1 0 4 0 2 0
## INS 0 0 0 0 0 0
## STIM1 1910 1632 2482 841 3723 1260
## TRIM22 3818 717 1568 1600 1466 890
## PTH 0 0 0 0 0 0
## SAA1 570 610 51 4363 315 2227
## SLC1A2 33 70 117 46 128 68
## FOSL1 330 39 180 51 26 21
## CCND1 2250 4582 6446 1818 8983 3416
## JRKL 158 193 218 142 394 190
## MMP7 321 160 619 646 1518 331
## MMP1 1172 39 11 66 5 56
## MMP3 95 2 27 15 17 11
## CASP1 2226 655 828 1727 1186 967
## DLAT 1161 1202 739 853 1325 788
## IL18 1217 1055 1614 2093 1829 1670
## CD3E 577 222 233 245 267 294
## MCAM 2758 3206 2207 5754 4107 1680
## WNK1 3185 5053 5910 1602 7206 3186
## TNFRSF1A 4737 2495 3369 2436 4345 2012
## GAPDH 41908 17087 20077 34930 38333 20033
## CD4 973 837 1211 760 1466 954
## SLC2A3 2110 226 453 280 532 223
## CLEC4D 37 4 0 4 2 2
## KLRB1 134 34 32 145 51 82
## CD69 448 53 119 144 45 65
## CLEC2B 975 350 845 3312 1218 733
## OLR1 155 6 6 11 4 22
## ABCD2 23 52 48 173 59 71
## LRRK2 419 275 632 284 536 262
## VDR 1293 2366 1337 362 1937 1231
## TMBIM6 12545 26086 11097 12465 20893 15185
## PFDN5 5143 3206 3441 13042 5813 4924
## SP7 0 1 9 0 6 2
## MUCL1 4079 2662 5375 1413 10709 2413
## CD63 11169 7816 10906 22446 14399 10461
## SUOX 1566 2478 1913 1104 3222 1729
## RPS26 10199 6994 1969 17946 9689 12911
## RPL41 54089 33667 30116 131780 53402 51696
## IL23A 44 11 19 12 18 14
## DDIT3 386 260 345 357 551 277
## IFNG 42 1 1 6 6 2
## IL22 91 0 1 5 0 0
## LYZ 8054 2918 2859 10922 3461 3306
## CAPS2 42 51 67 29 127 74
## SYT1 2 20 35 3 33 25
## BTG1 6492 3132 3867 6421 6813 5256
## HCAR3 4463 191 212 474 411 160
## GJB2 50370 5171 2506 9787 3164 6385
## GJB6 15630 2040 1996 4043 2262 1958
## TNFSF11 8 25 1 6 13 22
## TPT1 108758 50898 52107 224029 81914 86674
## LMO7 1177 862 1258 945 1713 469
## TNFSF13B 348 150 173 446 223 238
## LAMP1 4632 6984 6200 2379 8578 4644
## PSME2 3445 957 959 3392 1550 1109
## GZMB 969 11 20 64 15 31
## RPL36AL 4858 2591 1973 6346 3412 3197
## PYGL 3493 986 1115 1969 1515 914
## HIF1A 5929 1632 2041 1376 3939 1248
## FOS 900 317 805 398 756 325
## JDP2 793 529 1673 703 1403 483
## NOXRED1 17 17 28 14 51 12
## SERPINA1 1276 151 156 310 217 161
## CDC42BPB 2181 2254 3202 1054 4371 1299
## PLA2G4D 6144 1451 349 1865 472 307
## TRIM69 31 19 14 23 22 6
## SLC51B 26 37 43 52 60 35
## SMAD3 896 794 1284 559 2005 805
## CYP1A1 25 83 10 2 92 9
## AKAP13 2268 2419 3797 1021 5133 1996
## MEFV 276 7 23 12 42 6
## SOCS1 404 76 173 126 224 109
## ATXN2L 1313 1267 1623 565 2776 864
## CD19 11 4 4 9 19 4
## ITGAL 456 269 267 165 284 212
## ITGAM 297 170 353 260 497 201
## ITGAX 661 160 423 125 482 182
## DNAJA2 3005 1853 2128 3337 2734 1729
## SIAH1 654 489 701 559 1096 561
## ADCY7 1073 891 1615 408 1691 639
## NOD2 1088 346 492 390 480 314
## CMTM2 19 4 4 16 9 3
## SF3B3 2892 2948 2395 1563 4281 2273
## PSMD7 3154 2801 1877 3369 2931 2177
## WWOX 113 119 218 131 232 213
## MAF 2931 2127 4266 2184 5465 2196
## SLC7A5 4218 3736 929 740 1600 771
## CXCL16 1449 852 705 565 1003 656
## XAF1 2165 297 806 683 923 379
## CD68 1627 607 1081 1293 1097 658
## PER1 2656 2706 5892 1230 7227 1971
## NOS2 991 7 32 15 42 6
## TRAF4 823 657 839 551 1492 397
## CCL2 951 137 322 600 284 212
## CCL5 708 127 179 367 275 203
## CCL3 359 10 9 28 4 7
## CCR7 375 124 86 249 178 135
## STAT3 13310 6699 5917 4902 9534 4918
## SOST 76 27 97 12 4 0
## ITGA2B 24 18 21 18 28 4
## EFCAB13 39 53 80 37 149 56
## NPEPPS 3425 2106 2029 1650 3259 1536
## TBX21 21 17 9 2 17 9
## COL1A1 56608 46382 132174 8619 158539 78663
## MRPS23 1135 595 561 1859 925 633
## MIR21 2 0 1 2 1 1
## RPS6KB1 1044 749 937 662 1187 630
## ACE 582 364 800 284 1020 229
## ERN1 503 615 647 252 974 370
## PRTN3 1 0 0 0 2 0
## PLIN5 437 4970 72 224 1430 998
## RETN 31 19 3 10 3 1
## CCL25 2 2 0 0 1 0
## ICAM1 2754 425 847 360 829 355
## TYK2 1261 1107 1649 590 2371 721
## SMARCA4 1898 2251 2227 784 4048 1399
## ACP5 1406 1658 635 966 1288 985
## JUNB 5746 3195 2358 2165 3355 1547
## CYP4F22 1572 3450 831 784 1850 1210
## JAK3 539 221 219 119 331 145
## JUND 2146 2450 4343 1141 3698 1211
## CEBPA 4421 7223 4690 5265 6207 3620
## CEBPG 2055 1243 1769 1624 2368 1192
## ACP7 2634 1355 245 868 255 252
## ZFP36 5160 1016 1568 665 1848 620
## TGFB1 1531 728 1402 802 1389 654
## RPS19 25908 14487 15763 42926 27544 19306
## PSG2 1 0 1 1 3 3
## FUT2 422 107 128 189 247 73
## NKG7 258 56 42 148 75 43
## ZNF415 76 135 119 88 265 181
## LILRB2 376 62 123 125 81 41
## LILRA5 187 7 7 31 4 3
## KIR3DL1 0 0 0 0 0 0
## KIR3DL2 3 0 0 0 0 4
## SMOX 1346 835 353 277 428 182
## BMP2 612 195 598 168 479 214
## GINS1 340 144 141 403 207 140
## PLCG1 1826 1621 2003 827 2970 1017
## YWHAB 9517 7984 6484 7514 10565 7348
## PI3 45827 2487 81 14188 85 727
## MMP9 324 332 132 278 147 140
## CD40 601 247 431 586 557 244
## PFDN4 491 234 274 1257 424 399
## BMP7 670 676 759 412 1170 393
## RPS21 12137 6122 5740 34443 10986 10466
## MX1 5529 384 476 867 1015 309
## PFKL 3162 4300 3969 1815 6480 2849
## IL17RA 764 879 918 330 1239 619
## UBE2L3 2809 1966 1629 2604 2535 1818
## TPST2 811 496 852 574 1100 445
## XBP1 5441 5643 4628 4705 5778 3115
## UQCR10 1401 1352 661 1978 1377 1084
## SEC14L2 559 540 341 181 566 507
## APOL6 3138 511 812 1609 1204 654
## APOL1 2380 229 305 435 417 273
## TYMP 16860 2857 1428 4108 2010 1782
## BEND2 0 1 0 2 1 0
## PHEX 54 20 25 30 48 14
## TIMP1 1633 1224 1812 1518 2232 1432
## FOXP3 155 99 109 64 146 75
## MSN 10103 5060 6937 5631 8137 4851
## PGK1 15000 8126 6431 15660 9491 7760
## LAMP2 4552 4562 4268 4117 6334 4632
## CD40LG 20 41 32 36 54 51
## IRAK1 3479 2741 2576 1724 3269 1534
## COX2 14627 30865 29169 28465 68381 30865
## GSM5629989 GSM5629990 GSM5629991 GSM5629992 GSM5629993 GSM5629994
## TNFRSF9 22 18 9 47 29 38
## ENO1 17723 21601 15229 20754 19095 24675
## PIK3CD 165 152 164 287 463 363
## PGD 2640 4366 5215 2469 3655 2800
## MTHFR 366 267 326 806 898 739
## TNFRSF1B 422 371 401 773 949 902
## PINK1 775 1706 1435 1152 2649 1365
## IFNLR1 149 120 120 257 266 210
## RUNX3 383 730 418 715 1052 463
## SH3BGRL3 1769 2500 2460 3921 3663 4584
## CD52 405 263 152 249 225 331
## IFI6 13438 769 497 2147 607 8260
## ZC3H12A 2333 373 560 3862 838 4134
## UTP11 1313 804 517 807 729 1174
## JUN 505 948 692 943 1743 1135
## KANK4 13 73 152 15 101 31
## EFCAB7 143 159 137 114 190 125
## IL23R 6 11 5 6 10 8
## ADGRL2 492 834 476 680 1198 812
## GBP3 766 380 143 721 505 939
## GBP1 1011 438 241 1189 541 1695
## GBP5 170 27 39 238 149 188
## TGFBR3 725 1607 1942 1277 2480 1304
## VCAM1 277 484 158 270 454 607
## PTPN22 76 42 50 73 119 87
## CD160 3 3 7 11 16 16
## FCGR1A 17 5 7 23 4 81
## MCL1 5043 4004 2521 4191 4600 5772
## CTSK 6387 7690 8968 8406 14062 10665
## RORC 147 645 593 118 976 194
## S100A9 389117 4195 478 210657 6756 285663
## S100A12 2275 7 1 1057 3 1197
## S100A8 526708 5374 626 195026 5733 256069
## IL6R 470 330 339 447 741 626
## RIT1 918 597 372 630 673 746
## BGLAP 13 15 8 15 14 12
## IFI16 5065 1612 1127 4331 2483 5656
## AIM2 45 15 2 31 14 95
## CRP 6 1 1 0 0 1
## FCGR2A 117 97 89 265 230 349
## HSPA6 123 97 39 246 130 248
## FCGR3A 65 17 31 116 66 397
## SELL 212 37 32 99 68 186
## GLUL 12046 10945 7668 13627 11979 15096
## PTGS2 106 14 21 51 50 70
## CRB1 0 3 1 2 3 2
## KDM5B 1488 1216 1456 1694 2537 1653
## IL10 12 6 2 16 13 34
## YOD1 1391 569 242 2597 1076 2142
## HHAT 139 195 143 199 215 173
## TRAF5 100 104 87 159 311 180
## NLRP3 24 23 9 48 64 88
## LINC01250 0 0 0 0 0 0
## RPS7 43909 34935 22944 21835 18365 32420
## RSAD2 645 96 50 142 110 933
## FOSL2 1452 2307 2234 4636 4742 4437
## REL 1070 527 560 1955 1135 1304
## TGFA 849 635 503 1088 914 1260
## DYSF 101 235 189 331 321 378
## HK2 786 739 854 2825 2083 2498
## CD8A 313 198 75 193 123 188
## CD8B 106 103 37 111 41 53
## EIF5B 3218 3235 3655 3867 3808 3790
## IL1R1 1621 1426 1083 2028 2558 2125
## RGPD6 186 184 153 258 460 241
## IL1A 48 30 16 15 63 12
## IL1B 163 40 31 76 52 71
## IL37 65 376 140 222 981 170
## IL36RN 5327 593 200 8854 1052 8249
## IL1F10 28 48 7 30 91 40
## IL1RN 2222 1813 1186 1944 1284 1909
## NMI 1602 510 297 967 417 1344
## TNFAIP6 66 75 41 83 59 90
## IFIH1 1158 326 229 600 494 1052
## SCN1A 0 0 1 1 5 0
## ABCB11 11 50 57 6 39 22
## RBM45 144 116 118 144 164 176
## FRZB 359 884 519 285 710 460
## TFPI 492 453 374 381 571 645
## STAT1 5177 1903 1351 4526 2250 5741
## NABP1 419 115 68 294 282 399
## SF3B1 3975 3205 2820 5039 6579 5107
## CASP10 383 155 157 304 306 343
## CD28 100 30 26 95 96 166
## CTLA4 59 7 2 75 18 50
## ICOS 103 17 5 44 30 53
## NDUFS1 2395 2641 2658 2254 3948 2644
## CXCR2 360 107 103 340 214 301
## IRS1 126 467 419 465 840 304
## CCL20 159 11 4 49 28 62
## ATG16L1 448 414 349 424 795 556
## GPR35 13 6 9 19 40 27
## PDCD1 38 5 9 21 33 29
## PPARG 161 746 981 127 678 378
## RPL15 43907 53145 34352 27500 32564 41206
## EOMES 15 10 16 15 27 42
## CX3CR1 87 78 113 75 239 177
## ACKR2 60 23 27 181 40 246
## CCR2 204 209 87 148 368 391
## CCRL2 33 12 16 27 14 27
## TLR9 12 2 5 9 17 17
## ARHGEF3 962 645 438 793 793 994
## ADAMTS9 65 98 85 93 165 176
## TMEM45A 12168 14172 9160 13508 14337 15068
## CD80 15 4 3 13 5 10
## CD86 127 129 75 168 168 249
## MIX23 423 228 190 244 224 344
## PLS1 189 153 71 145 129 124
## PTX3 37 20 22 32 39 117
## GOLIM4 468 797 587 1024 1452 986
## MYNN 797 627 358 491 585 656
## TNFSF10 5241 1609 1087 2641 2038 4218
## ADIPOQ 277 130 218 340 1051 1725
## SPON2 166 508 402 608 854 846
## S100P 685 313 105 711 295 514
## WDR1 3553 5608 4009 5934 6611 6857
## CD38 32 6 4 32 18 30
## PPARGC1A 130 610 387 161 498 140
## TMPRSS11B 1 0 0 0 0 0
## CSN3 0 0 0 0 2 0
## ALB 0 0 2 0 2 0
## CXCL8 2568 6 0 944 0 837
## CXCL2 26 6 0 50 7 77
## AREG 235 146 54 89 113 65
## CXCL10 121 13 34 188 20 193
## CXCL13 82 8 3 140 16 180
## SPP1 15 29 29 32 8 170
## HERC6 1848 161 141 550 222 1444
## NFKB1 1267 907 730 1665 1688 1640
## SEC24B 793 671 669 775 1110 662
## EGF 18 60 38 16 63 16
## IL2 5 2 1 0 1 0
## IL21 5 0 0 2 0 5
## IL21-AS1 3 0 0 5 0 1
## SLC7A11 249 19 14 291 62 223
## IL15 68 66 40 79 75 66
## EDNRA 331 311 176 321 397 484
## TLR2 189 146 113 207 480 327
## FGB 0 0 0 0 0 0
## DDX60 1186 349 203 776 768 1350
## SPCS3 2951 2675 1632 2324 2294 2739
## TLR3 126 95 76 141 156 186
## OSMR 625 411 236 991 897 1087
## GZMK 37 23 24 26 70 78
## GZMA 172 40 38 97 68 199
## ANKRD55 19 5 3 1 7 5
## CENPK 210 76 33 182 81 194
## CAST 5689 6589 4762 6994 8720 6946
## ERAP1 1626 1892 1825 1900 2543 1860
## ERAP2 93 51 36 999 909 1081
## TNFAIP8 1625 901 630 1090 965 1199
## CSF2 16 2 2 0 3 8
## IL5 0 0 0 1 2 0
## IL13 7 5 1 3 3 3
## IL4 1 0 1 1 2 0
## CD14 364 477 398 814 855 1663
## CSNK1A1 9019 7265 4107 9920 6704 12188
## PPARGC1B 194 130 115 532 495 393
## TNIP1 1776 2115 1476 3198 3538 3827
## ATOX1 1190 677 426 686 493 880
## FAXDC2 591 1392 1771 753 3920 830
## IL12B 17 3 0 13 1 4
## MIR146A 2 0 0 6 1 5
## PDLIM7 264 456 340 486 725 848
## SERPINB1 2620 1033 780 2381 1065 2394
## SSR1 2925 3465 2588 2713 3201 3340
## CD83 340 159 99 278 263 213
## SOX4 643 773 890 694 1706 731
## CMAHP 121 172 185 145 315 148
## HLA-A 13119 13439 11472 13646 14936 19872
## HLA-C 17683 19988 11390 18243 21018 25553
## HLA-B 22771 22888 16402 20698 21321 33031
## MICA 264 201 194 250 367 322
## LTA 8 4 6 17 28 8
## TNF 40 38 18 59 65 55
## HLA-DRB1 4152 4070 2876 3716 3915 7716
## HLA-DQB1 483 574 399 441 616 1463
## PPARD 601 717 705 2296 1474 2048
## CCND3 1177 1089 898 896 1249 1210
## VEGFA 291 361 434 1173 1126 1041
## RUNX2 43 56 30 93 138 69
## IL17A 49 0 0 8 0 15
## IL17F 34 2 0 16 1 33
## PRDM1 1011 304 163 1630 791 1508
## ATG5 1000 913 542 640 700 926
## TRAF3IP2 599 881 583 930 942 1079
## NCOA7 1185 623 543 932 1091 1232
## SGK1 3815 1752 848 2761 1791 3808
## IFNGR1 4339 2789 1442 2950 2735 4553
## TNFAIP3 183 373 156 532 599 407
## SOD2 9900 7093 4170 9195 8981 10554
## LPAL2 5 1 1 3 13 7
## PLG 1 0 1 2 3 2
## CCR6 105 137 125 66 233 87
## RAC1 8562 7688 5764 6581 6234 7238
## ZNF316 173 346 368 804 1252 675
## AHR 2809 2095 1210 2596 3115 3079
## IL6 0 0 5 0 1 9
## TOMM7 4607 6408 4196 2902 3736 3526
## CYCS 6910 4368 3184 4227 3326 6041
## AQP1 4573 8811 9013 6112 8991 11429
## NT5C3A 2944 830 538 3208 907 2773
## EGFR 1494 2671 2934 3789 5772 2629
## CD36 5037 2175 1511 5458 2283 8622
## SAMD9 797 162 141 500 333 1060
## SERPINE1 78 118 38 115 60 223
## CUX1 606 944 750 1321 1311 1408
## PSMC2 3452 3120 2030 2664 2746 3284
## NAMPT 8973 3296 1926 8038 3030 9235
## HYAL4 374 30 1 218 31 98
## LEP 30 6 53 35 180 823
## CALD1 2827 4944 4468 3731 7075 6857
## BRAF 271 306 323 481 689 437
## EZH2 653 228 186 594 375 636
## DNAJB6 4755 4342 2836 5394 3875 6664
## CSMD1 11 16 27 1 39 21
## CTSB 11341 8801 5150 13734 14526 27791
## EGR3 432 889 912 835 1696 738
## TNFRSF10A 223 134 94 201 170 234
## BNIP3L 4447 4437 4592 4421 5646 5003
## DUSP4 176 509 914 339 913 375
## NRG1 35 207 55 88 170 134
## RPL7 62834 84934 64557 38642 49822 59560
## IL7 125 149 82 131 124 174
## GEM 167 180 123 183 294 322
## MYC 1536 932 628 1020 1174 1574
## GPT 97 1253 1936 189 1812 205
## JAK2 656 450 333 600 813 592
## CD274 251 27 8 156 59 204
## IL33 2515 1296 918 1443 1145 1947
## LURAP1L-AS1 2 3 2 2 3 5
## LURAP1L 387 419 177 275 381 446
## TTC39B 1583 1694 2001 1327 2542 1325
## IFNA1 0 0 0 0 0 0
## TEK 157 206 173 183 284 403
## TRBV20OR9-2 0 0 0 0 0 0
## TOMM5 1785 1445 959 1124 777 1506
## ANXA1 6700 8635 5136 7193 6883 10696
## ERP44 1195 1154 826 1076 1057 1244
## ZNF483 47 53 59 60 171 65
## TNFSF15 8 23 27 26 65 13
## TLR4 184 241 168 493 617 617
## PTGS1 1987 1929 1918 3364 3619 3312
## HSPA5 7503 8661 8463 8592 9861 9636
## FNBP1 888 1003 799 1704 1924 1508
## CARD9 70 76 28 126 131 158
## TRAF2 346 306 225 284 452 354
## CLIC3 996 848 305 1144 841 1240
## IL2RA 66 41 8 73 48 82
## GATA3 1350 2175 2762 1286 2993 922
## VIM 14352 23942 18920 23799 28072 27984
## CREM 397 404 254 234 331 351
## DKK1 14 25 24 36 17 41
## MBL2 0 0 0 0 0 0
## SAR1A 2206 2119 1677 1736 2096 2303
## PRF1 87 27 27 64 82 95
## ZMIZ1 442 963 1079 1856 2535 1489
## IFIT3 1788 345 298 1007 382 2494
## TALDO1 4147 5495 5505 3423 3949 4146
## IGF2 60 273 245 270 301 657
## INS-IGF2 56 266 236 254 280 588
## IGF2-AS 0 0 1 0 0 1
## INS 0 0 0 0 0 0
## STIM1 860 1671 1356 1763 2293 1828
## TRIM22 2115 547 315 1487 1032 2139
## PTH 0 0 0 0 0 0
## SAA1 1189 1376 784 270 647 1263
## SLC1A2 24 22 21 21 64 32
## FOSL1 87 77 6 91 17 104
## CCND1 976 2945 3513 1555 5616 1961
## JRKL 119 137 130 102 330 168
## MMP7 530 766 498 672 817 349
## MMP1 8 3 6 17 13 35
## MMP3 3 6 6 8 20 57
## CASP1 1799 1017 474 1386 852 1553
## DLAT 803 978 862 809 1164 950
## IL18 2474 2334 1280 1710 1898 1800
## CD3E 347 205 170 214 318 226
## MCAM 906 2016 2408 1403 3035 3829
## WNK1 1280 2477 2184 4094 5743 3362
## TNFRSF1A 2966 2767 2284 2861 3269 3749
## GAPDH 35817 32991 21462 28214 26668 41644
## CD4 398 673 479 974 1288 1254
## SLC2A3 293 247 149 267 335 375
## CLEC4D 3 0 1 1 0 5
## KLRB1 151 93 36 70 99 123
## CD69 74 51 26 58 62 87
## CLEC2B 2855 898 811 2045 911 2950
## OLR1 5 3 3 9 16 4
## ABCD2 20 11 13 39 74 78
## LRRK2 159 163 108 277 466 252
## VDR 568 1029 1301 823 1662 794
## TMBIM6 11920 24829 23794 9859 20884 14266
## PFDN5 8470 8322 5210 4543 4602 7178
## SP7 0 2 5 0 0 1
## MUCL1 2835 6819 7282 1532 4698 3594
## CD63 9427 14807 9719 9781 11730 14760
## SUOX 1084 2144 1988 1338 2587 1466
## RPS26 21274 21674 10424 8357 8087 17646
## RPL41 102119 103528 63206 51378 49934 80906
## IL23A 12 5 5 19 7 19
## DDIT3 376 225 221 296 329 286
## IFNG 7 0 3 11 2 4
## IL22 16 0 0 1 1 8
## LYZ 10225 4908 1662 6792 3693 8703
## CAPS2 20 56 40 44 80 29
## SYT1 3 14 6 25 43 6
## BTG1 8609 5369 4742 5298 5208 6262
## HCAR3 423 193 136 451 193 381
## GJB2 21045 2701 1235 23164 5432 30694
## GJB6 6192 1454 658 7651 2132 9672
## TNFSF11 15 18 11 9 17 11
## TPT1 180074 149551 92372 100663 80922 150615
## LMO7 857 625 491 1653 1140 1330
## TNFSF13B 200 195 102 239 199 321
## LAMP1 2316 5424 6263 5294 7230 5102
## PSME2 2489 1531 1021 1742 1163 2577
## GZMB 221 16 10 97 18 153
## RPL36AL 6600 5729 3675 3232 3065 4314
## PYGL 1917 930 667 2183 1169 2899
## HIF1A 1828 1687 1344 2030 2285 2741
## FOS 257 300 268 292 783 375
## JDP2 434 651 401 673 845 780
## NOXRED1 9 8 6 15 33 12
## SERPINA1 253 60 44 282 139 452
## CDC42BPB 566 1106 1016 2519 2738 2164
## PLA2G4D 3666 169 85 6630 727 5880
## TRIM69 10 10 5 19 29 28
## SLC51B 14 16 18 19 39 31
## SMAD3 516 532 769 1000 1474 864
## CYP1A1 210 369 14 0 115 3
## AKAP13 577 1262 1217 2289 3305 1726
## MEFV 26 7 10 17 18 47
## SOCS1 135 79 73 101 107 135
## ATXN2L 390 476 412 1110 1746 980
## CD19 12 2 2 2 10 4
## ITGAL 132 65 76 205 362 207
## ITGAM 86 188 114 323 360 317
## ITGAX 79 55 19 149 180 181
## DNAJA2 3102 2116 1474 2186 2195 2751
## SIAH1 452 436 374 586 610 534
## ADCY7 271 320 293 1215 1521 922
## NOD2 503 258 127 968 510 1196
## CMTM2 8 0 3 2 5 4
## SF3B3 1826 2273 2035 2375 3078 2589
## PSMD7 3217 3172 2510 2680 2687 3081
## WWOX 400 239 183 164 189 323
## MAF 2176 1707 2103 3626 4213 3565
## SLC7A5 2005 1713 1344 2909 1458 2024
## CXCL16 528 718 454 762 1085 793
## XAF1 1326 173 153 852 495 1347
## CD68 698 632 394 1519 1177 1810
## PER1 485 1346 895 2694 1964 1644
## NOS2 97 4 2 230 9 91
## TRAF4 548 507 478 497 814 615
## CCL2 347 133 94 242 295 593
## CCL5 182 90 133 158 143 164
## CCL3 44 1 2 55 7 14
## CCR7 147 36 29 106 97 144
## STAT3 5696 5225 3939 8801 7211 8622
## SOST 19 1 0 27 0 63
## ITGA2B 7 8 6 8 14 22
## EFCAB13 12 19 21 45 95 32
## NPEPPS 1481 1389 1048 1904 2177 2184
## TBX21 11 6 2 15 10 17
## COL1A1 6893 21806 37203 137114 65821 98470
## MRPS23 1345 926 688 863 744 1153
## MIR21 1 1 1 0 1 1
## RPS6KB1 683 649 508 763 946 916
## ACE 141 260 263 508 396 659
## ERN1 206 220 232 490 663 379
## PRTN3 1 0 1 0 0 0
## PLIN5 272 608 1020 480 2043 483
## RETN 0 3 2 0 4 2
## CCL25 0 0 0 0 0 1
## ICAM1 325 265 332 449 519 558
## TYK2 393 387 296 1029 1464 1029
## SMARCA4 785 1217 1499 2232 2346 1704
## ACP5 1041 1138 830 1650 1102 1711
## JUNB 3543 2083 1082 3227 2771 4821
## CYP4F22 928 1250 2074 2196 2345 1790
## JAK3 100 49 50 180 203 227
## JUND 594 2089 1414 1710 2198 1530
## CEBPA 5038 5631 4895 5211 5760 5267
## CEBPG 1915 1602 1078 1544 1506 1857
## ACP7 1279 135 63 3029 843 3021
## ZFP36 804 487 375 1422 924 1327
## TGFB1 564 671 575 886 1214 1164
## RPS19 35230 33195 23210 20140 20653 33149
## PSG2 1 3 0 5 9 4
## FUT2 385 64 49 378 130 337
## NKG7 35 14 39 45 41 106
## ZNF415 94 162 129 53 147 96
## LILRB2 51 27 26 148 106 168
## LILRA5 7 3 0 22 2 27
## KIR3DL1 2 0 1 0 2 0
## KIR3DL2 2 0 1 0 2 0
## SMOX 360 224 107 1021 558 1221
## BMP2 200 301 150 192 218 268
## GINS1 461 158 101 211 179 308
## PLCG1 764 581 628 1879 2189 1612
## YWHAB 8282 10589 9261 7801 9021 8924
## PI3 42807 161 38 26360 441 25482
## MMP9 126 92 99 316 355 365
## CD40 338 397 218 367 337 445
## PFDN4 1147 566 434 510 324 700
## BMP7 532 647 564 697 795 699
## RPS21 24778 21814 13710 12105 9445 17933
## MX1 2311 295 230 1589 564 3445
## PFKL 1352 2893 3131 2983 5147 3118
## IL17RA 267 412 515 585 957 630
## UBE2L3 2550 2389 1918 1975 2000 2467
## TPST2 544 491 296 711 657 725
## XBP1 3812 5514 4210 4037 5772 5057
## UQCR10 1984 1889 1607 1047 1264 1567
## SEC14L2 277 361 212 324 592 369
## APOL6 1155 381 467 1302 856 1847
## APOL1 740 131 142 922 333 979
## TYMP 4799 1591 931 5752 2427 10372
## BEND2 4 0 0 0 0 0
## PHEX 59 19 9 62 25 20
## TIMP1 863 1211 1147 1158 1528 2270
## FOXP3 62 48 52 100 97 110
## MSN 3868 5913 3318 7029 7330 8603
## PGK1 12987 10277 6596 10705 9414 13961
## LAMP2 5325 6208 4424 3936 5384 4688
## CD40LG 36 35 26 17 82 34
## IRAK1 1233 1666 1672 2956 3194 2826
## COX2 15067 25537 30774 41964 39726 22822
## GSM5629995 GSM5629996 GSM5629998 GSM5629999 GSM5630000 GSM5630001
## TNFRSF9 37 10 16 16 96 14
## ENO1 23460 8939 10057 9220 18420 15653
## PIK3CD 423 278 321 187 267 327
## PGD 3877 1730 1691 2166 2915 2151
## MTHFR 722 634 691 449 591 631
## TNFRSF1B 1053 600 730 629 753 772
## PINK1 2827 1429 2512 1660 735 1632
## IFNLR1 231 221 164 184 203 276
## RUNX3 1073 439 549 526 456 822
## SH3BGRL3 4440 2552 2672 2300 3478 4239
## CD52 346 188 222 167 233 217
## IFI6 1348 695 468 1008 3464 748
## ZC3H12A 1056 412 299 240 4680 559
## UTP11 816 576 640 582 906 586
## JUN 2286 1445 1243 1178 824 1355
## KANK4 135 79 45 59 16 75
## EFCAB7 178 150 139 105 96 162
## IL23R 4 1 1 1 6 3
## ADGRL2 997 968 808 839 806 939
## GBP3 600 280 59 39 741 476
## GBP1 649 428 321 576 1314 436
## GBP5 187 77 160 89 287 76
## TGFBR3 2734 1876 2924 2266 704 2968
## VCAM1 623 355 180 321 364 406
## PTPN22 83 45 43 23 110 57
## CD160 25 14 13 11 10 10
## FCGR1A 33 4 11 11 43 6
## MCL1 6052 4251 3465 4292 6547 4870
## CTSK 14052 14545 26865 11720 6228 13603
## RORC 724 462 793 602 95 578
## S100A9 5435 1109 201 234 237778 935
## S100A12 6 4 0 1 1818 1
## S100A8 4960 799 132 269 217804 802
## IL6R 773 520 642 449 512 626
## RIT1 739 467 535 505 612 465
## BGLAP 22 29 10 7 6 20
## IFI16 2254 1750 1972 2313 4781 2425
## AIM2 23 14 15 7 37 5
## CRP 0 0 0 0 0 0
## FCGR2A 220 146 202 141 360 142
## HSPA6 118 82 98 113 122 133
## FCGR3A 174 52 53 36 200 32
## SELL 133 65 35 44 207 24
## GLUL 12409 9392 10631 8371 19868 11183
## PTGS2 33 27 40 55 237 13
## CRB1 6 2 4 1 3 3
## KDM5B 1873 1605 1287 1451 1676 1855
## IL10 17 6 22 2 17 11
## YOD1 862 1108 874 1178 1691 1078
## HHAT 161 125 135 131 95 133
## TRAF5 312 244 219 130 137 237
## NLRP3 73 30 40 24 60 31
## LINC01250 0 0 0 0 0 0
## RPS7 32566 20942 26657 19503 18927 19351
## RSAD2 88 82 54 87 515 106
## FOSL2 3943 3202 3148 4580 3397 4352
## REL 853 773 852 912 1313 800
## TGFA 1054 953 714 594 2714 576
## DYSF 369 227 249 312 254 350
## HK2 1444 822 522 718 2972 811
## CD8A 148 63 75 54 236 86
## CD8B 59 39 25 46 79 62
## EIF5B 3849 3424 3269 3904 2971 3688
## IL1R1 2222 1949 2698 2011 1855 2392
## RGPD6 417 449 440 296 234 499
## IL1A 50 40 24 39 140 78
## IL1B 23 14 8 14 1012 11
## IL37 1033 809 1110 988 23 776
## IL36RN 1031 1065 1034 1439 17388 1300
## IL1F10 76 100 84 50 45 101
## IL1RN 1488 1382 991 1393 4886 1445
## NMI 563 430 364 420 723 459
## TNFAIP6 83 56 212 88 190 81
## IFIH1 497 416 350 445 674 402
## SCN1A 3 10 4 8 1 2
## ABCB11 50 22 65 56 12 34
## RBM45 144 130 130 106 156 141
## FRZB 1929 764 650 392 129 510
## TFPI 666 656 756 572 338 667
## STAT1 2546 1619 1505 2767 4629 2092
## NABP1 275 253 272 105 498 204
## SF3B1 6142 5140 5898 4031 4274 5661
## CASP10 326 249 242 275 392 268
## CD28 111 59 100 31 126 61
## CTLA4 56 21 27 26 82 28
## ICOS 50 12 30 21 70 10
## NDUFS1 3646 1849 1838 1970 2473 2314
## CXCR2 191 114 39 86 648 108
## IRS1 616 634 671 684 322 788
## CCL20 52 45 4 3 274 24
## ATG16L1 838 461 641 456 498 572
## GPR35 44 26 36 10 34 39
## PDCD1 39 13 7 6 49 7
## PPARG 708 430 488 384 148 356
## RPL15 50918 33420 38026 32289 24092 33050
## EOMES 60 6 13 8 35 7
## CX3CR1 240 88 110 89 90 81
## ACKR2 32 19 36 23 360 32
## CCR2 430 185 241 105 247 211
## CCRL2 36 10 17 22 49 22
## TLR9 28 8 16 2 17 24
## ARHGEF3 859 633 755 732 677 740
## ADAMTS9 145 234 136 212 121 272
## TMEM45A 15918 7116 9572 11341 14853 7588
## CD80 12 2 3 5 31 4
## CD86 214 90 154 79 173 134
## MIX23 220 187 197 160 309 179
## PLS1 142 67 105 92 202 149
## PTX3 83 56 42 53 96 53
## GOLIM4 1349 1267 1140 1035 832 1705
## MYNN 658 508 531 480 511 527
## TNFSF10 2162 1874 1795 1299 2834 1823
## ADIPOQ 577 1979 671 721 153 598
## SPON2 1021 914 918 587 245 1177
## S100P 334 541 705 78 749 405
## WDR1 7228 4393 4500 4824 6215 5784
## CD38 27 7 10 5 42 3
## PPARGC1A 851 221 399 164 166 497
## TMPRSS11B 0 0 0 0 0 0
## CSN3 0 0 0 0 0 0
## ALB 8 7 0 2 1 0
## CXCL8 4 0 5 0 6267 3
## CXCL2 7 5 8 6 93 4
## AREG 249 82 45 31 278 79
## CXCL10 64 11 1 15 178 1
## CXCL13 22 22 43 2 238 5
## SPP1 36 22 18 4 28 7
## HERC6 238 176 117 252 844 258
## NFKB1 1373 1056 1056 1288 1778 1425
## SEC24B 993 745 821 771 584 910
## EGF 151 72 61 58 14 61
## IL2 3 0 1 2 3 0
## IL21 0 0 1 0 15 0
## IL21-AS1 1 1 0 2 5 0
## SLC7A11 23 74 29 56 362 65
## IL15 97 62 44 70 65 69
## EDNRA 513 502 428 458 284 577
## TLR2 431 158 187 70 324 168
## FGB 0 0 0 0 0 0
## DDX60 757 512 413 880 928 605
## SPCS3 2643 1571 2118 1818 2443 2024
## TLR3 151 157 111 151 64 161
## OSMR 900 601 514 634 1212 662
## GZMK 112 19 34 25 69 26
## GZMA 142 53 45 25 115 26
## ANKRD55 9 16 4 2 3 13
## CENPK 77 70 48 66 168 82
## CAST 8626 6636 7644 7237 5157 8338
## ERAP1 2273 1702 1675 2155 1220 2547
## ERAP2 890 141 721 1430 86 123
## TNFAIP8 1013 915 903 927 957 925
## CSF2 3 0 1 1 6 4
## IL5 2 3 1 1 1 2
## IL13 2 4 1 0 1 1
## IL4 1 1 1 1 0 3
## CD14 1410 463 1196 598 751 502
## CSNK1A1 7359 5489 5389 7232 9575 6543
## PPARGC1B 366 170 243 193 473 254
## TNIP1 3106 2204 2404 1767 3923 2397
## ATOX1 672 505 544 519 755 466
## FAXDC2 2914 1456 1909 1207 995 1499
## IL12B 6 0 0 3 52 0
## MIR146A 0 2 0 1 3 0
## PDLIM7 1171 834 698 518 435 783
## SERPINB1 1423 754 1012 850 5653 927
## SSR1 3569 2172 2175 2449 2764 2932
## CD83 228 141 98 107 401 176
## SOX4 1194 1335 1512 919 559 1301
## CMAHP 319 443 394 329 129 628
## HLA-A 21647 16232 14467 15621 12047 15284
## HLA-C 27266 12469 11826 12536 17883 22924
## HLA-B 32150 19311 21908 25826 21225 26116
## MICA 405 224 494 280 130 327
## LTA 24 17 9 3 26 11
## TNF 61 42 49 37 72 31
## HLA-DRB1 7657 3763 4986 6017 3758 3774
## HLA-DQB1 1715 2702 1235 1289 2479 2443
## PPARD 1538 767 829 909 2346 976
## CCND3 1599 840 793 742 961 945
## VEGFA 1265 874 456 409 983 1080
## RUNX2 100 113 134 79 65 135
## IL17A 0 0 0 0 38 2
## IL17F 1 0 0 0 49 0
## PRDM1 647 593 526 531 1942 659
## ATG5 882 621 692 638 635 623
## TRAF3IP2 958 753 797 846 941 951
## NCOA7 860 540 663 589 1369 850
## SGK1 2814 1284 1224 1064 3323 1975
## IFNGR1 2918 2130 2770 1833 4643 2231
## TNFAIP3 432 495 343 394 665 539
## SOD2 6728 4095 5730 3389 19021 5414
## LPAL2 10 10 18 13 6 11
## PLG 4 1 2 2 1 2
## CCR6 123 88 99 93 80 94
## RAC1 6611 5342 5643 5897 5838 6698
## ZNF316 1116 1087 1174 678 560 1127
## AHR 2590 2224 2666 2696 2299 2378
## IL6 5 4 0 2 5 1
## TOMM7 5397 4530 5419 4559 2090 3536
## CYCS 4236 1994 2461 2361 6149 2342
## AQP1 13057 9833 6911 10359 3777 11653
## NT5C3A 983 702 806 740 2465 776
## EGFR 4938 3913 4777 5124 2727 6976
## CD36 3881 2768 2351 1619 2571 1698
## SAMD9 291 254 184 263 1076 257
## SERPINE1 102 51 42 87 58 133
## CUX1 1296 1438 900 1251 1066 1765
## PSMC2 3449 1954 2309 2321 2597 2160
## NAMPT 3149 1419 2123 1877 13443 1733
## HYAL4 37 22 34 5 273 16
## LEP 296 392 444 317 36 175
## CALD1 9221 8032 6859 6972 3076 8055
## BRAF 642 668 634 572 359 749
## EZH2 415 364 302 212 527 407
## DNAJB6 4086 2553 2886 2968 4817 3106
## CSMD1 48 60 19 33 18 53
## CTSB 16890 7708 8775 6540 14259 10378
## EGR3 1952 1062 952 1080 948 1140
## TNFRSF10A 173 134 156 119 262 152
## BNIP3L 5813 3102 4387 3572 3584 3469
## DUSP4 1140 474 615 533 446 557
## NRG1 280 97 116 162 122 186
## RPL7 74332 54226 61206 53634 35146 59688
## IL7 164 162 151 215 118 195
## GEM 224 189 182 251 184 318
## MYC 1324 707 814 1037 1632 1065
## GPT 1539 625 606 550 223 508
## JAK2 806 612 642 677 621 686
## CD274 67 46 50 30 522 39
## IL33 1886 2083 1681 2033 2417 1652
## LURAP1L-AS1 4 3 0 2 1 1
## LURAP1L 434 443 322 323 229 389
## TTC39B 1961 865 1396 1215 1588 912
## IFNA1 0 0 0 0 0 0
## TEK 391 378 288 395 117 327
## TRBV20OR9-2 0 0 0 0 0 2
## TOMM5 1256 853 871 794 1154 793
## ANXA1 9098 7426 6716 7767 18430 10306
## ERP44 1162 842 884 899 1018 1004
## ZNF483 188 162 153 96 35 153
## TNFSF15 41 38 10 31 71 31
## TLR4 648 316 842 384 318 437
## PTGS1 3428 2633 3406 4885 1635 2836
## HSPA5 9427 5155 4675 6033 8960 7407
## FNBP1 1882 1499 1536 1432 1246 1683
## CARD9 142 60 61 52 115 119
## TRAF2 420 320 269 214 303 416
## CLIC3 1223 914 966 831 1002 869
## IL2RA 78 38 68 35 128 24
## GATA3 2383 3320 2602 3907 641 3808
## VIM 31721 28755 33948 25707 10191 35436
## CREM 418 243 305 286 308 272
## DKK1 32 27 232 21 21 56
## MBL2 0 0 0 0 0 0
## SAR1A 2474 2104 1905 1897 1633 2118
## PRF1 94 34 36 35 128 53
## ZMIZ1 2324 1781 2064 1519 1674 2545
## IFIT3 524 470 485 609 953 413
## TALDO1 4814 2529 2712 2649 3456 2807
## IGF2 357 441 246 481 148 349
## INS-IGF2 334 422 225 456 138 323
## IGF2-AS 0 1 2 4 0 0
## INS 0 0 0 0 0 0
## STIM1 2599 1653 1808 1676 1102 2303
## TRIM22 998 835 658 716 1256 740
## PTH 0 0 0 0 0 0
## SAA1 592 427 750 222 908 233
## SLC1A2 118 63 35 70 24 85
## FOSL1 72 13 16 7 215 22
## CCND1 4992 5678 5436 7121 1756 7504
## JRKL 288 267 175 137 143 310
## MMP7 1464 319 543 252 566 806
## MMP1 20 7 0 3 353 3
## MMP3 18 6 261 13 47 9
## CASP1 936 1076 719 917 1493 1052
## DLAT 1375 485 688 789 1014 844
## IL18 2475 2347 2583 2470 738 2239
## CD3E 241 160 220 137 286 218
## MCAM 3895 2578 1956 2537 1123 2263
## WNK1 4999 3846 3796 4694 2645 4658
## TNFRSF1A 3879 2378 2103 2408 3037 2844
## GAPDH 36803 15546 15955 16468 28531 22851
## CD4 1342 689 965 738 725 936
## SLC2A3 320 233 238 285 394 276
## CLEC4D 1 3 5 1 11 0
## KLRB1 103 25 51 33 66 37
## CD69 80 37 70 37 119 31
## CLEC2B 1344 1603 1581 1728 678 1409
## OLR1 5 0 0 1 19 0
## ABCD2 65 65 78 32 20 47
## LRRK2 373 348 417 330 159 188
## VDR 1566 1643 1079 1030 1468 1771
## TMBIM6 26438 12110 14459 12868 11479 13581
## PFDN5 8040 5575 6768 4884 3427 4578
## SP7 2 5 1 1 0 7
## MUCL1 21938 5297 4008 349 1854 2858
## CD63 18060 11271 12071 9257 6156 12149
## SUOX 2379 1805 1813 1929 1169 2025
## RPS26 20781 2978 9469 7413 8148 7812
## RPL41 88835 61954 72154 53761 44581 53862
## IL23A 10 4 6 4 61 3
## DDIT3 376 355 330 212 164 323
## IFNG 1 1 0 0 7 1
## IL22 1 0 0 0 6 0
## LYZ 6647 2031 1800 2473 4999 1670
## CAPS2 87 49 65 55 14 58
## SYT1 35 30 27 40 8 37
## BTG1 5088 5311 6008 5796 3385 5200
## HCAR3 199 85 172 273 837 186
## GJB2 3748 4614 1189 1179 36058 2879
## GJB6 1785 4051 822 820 12514 2961
## TNFSF11 10 7 10 4 12 6
## TPT1 133435 91471 110628 84477 87374 78614
## LMO7 1294 906 735 767 1432 1271
## TNFSF13B 279 167 56 141 189 187
## LAMP1 7345 5132 5194 5559 3518 6352
## PSME2 1563 1040 774 941 1864 1193
## GZMB 45 9 3 4 257 6
## RPL36AL 4439 4138 3865 3139 3513 3797
## PYGL 1318 1219 955 913 1302 1404
## HIF1A 2388 1396 1221 1243 3130 1987
## FOS 616 328 298 564 206 276
## JDP2 1001 833 1307 953 316 790
## NOXRED1 29 23 24 19 6 23
## SERPINA1 182 401 49 80 490 117
## CDC42BPB 2418 1935 2075 2037 1929 2551
## PLA2G4D 387 199 277 159 9588 279
## TRIM69 14 12 14 8 29 10
## SLC51B 55 33 30 19 9 35
## SMAD3 1219 1173 1475 1070 667 1504
## CYP1A1 7 18 25 604 28 318
## AKAP13 2742 2412 2571 2797 1450 2740
## MEFV 25 9 12 9 62 6
## SOCS1 159 125 96 75 266 86
## ATXN2L 1465 1194 1124 752 1079 1404
## CD19 17 13 6 2 11 4
## ITGAL 252 158 205 109 239 170
## ITGAM 354 225 308 177 165 409
## ITGAX 188 149 89 55 286 274
## DNAJA2 2733 1731 2004 2050 2474 1951
## SIAH1 596 563 642 514 358 598
## ADCY7 1220 832 893 641 752 1159
## NOD2 438 251 267 247 1096 406
## CMTM2 5 4 6 1 6 4
## SF3B3 2819 2180 1945 2194 2695 2847
## PSMD7 3195 1918 2386 2121 2407 2110
## WWOX 274 168 145 120 223 198
## MAF 2864 4092 3242 3414 1333 4104
## SLC7A5 1545 916 827 1341 2935 1374
## CXCL16 885 591 540 626 1124 702
## XAF1 465 555 336 409 851 470
## CD68 1320 812 1784 675 846 1489
## PER1 2060 2848 1858 2672 1012 3276
## NOS2 3 13 25 26 893 12
## TRAF4 1154 391 492 253 450 422
## CCL2 349 178 100 107 443 123
## CCL5 242 106 143 107 209 100
## CCL3 19 3 3 1 149 2
## CCR7 134 55 25 46 169 30
## STAT3 6880 3735 3784 4465 12399 5121
## SOST 83 0 0 2 162 40
## ITGA2B 15 15 9 7 6 12
## EFCAB13 64 73 94 44 29 67
## NPEPPS 1963 1583 1925 1427 2093 2041
## TBX21 9 8 4 13 11 7
## COL1A1 45240 75362 8119 66651 26498 87811
## MRPS23 1084 628 801 644 881 641
## MIR21 1 1 1 2 3 1
## RPS6KB1 899 635 751 778 721 804
## ACE 474 430 197 443 232 519
## ERN1 581 369 443 362 524 525
## PRTN3 2 0 0 0 0 0
## PLIN5 1340 333 545 267 530 278
## RETN 21 4 6 2 1 1
## CCL25 1 0 0 0 0 0
## ICAM1 699 371 480 423 716 372
## TYK2 1330 968 1068 609 805 984
## SMARCA4 2326 1799 1612 1643 1639 2255
## ACP5 1743 646 793 508 959 694
## JUNB 3634 2291 1989 1856 5163 1950
## CYP4F22 2558 728 1389 1160 2994 1083
## JAK3 217 113 110 72 301 112
## JUND 2783 1708 2588 2290 747 1863
## CEBPA 6092 4270 5220 5142 5432 4135
## CEBPG 1580 1356 1549 1597 1722 1450
## ACP7 522 240 344 255 5593 278
## ZFP36 1285 1022 829 750 1856 979
## TGFB1 1359 928 1023 793 824 1034
## RPS19 31669 23265 25432 20243 16294 20742
## PSG2 5 0 1 0 17 3
## FUT2 111 84 66 46 395 110
## NKG7 119 25 25 20 85 32
## ZNF415 150 165 156 174 61 191
## LILRB2 207 44 99 35 183 113
## LILRA5 37 1 3 0 55 5
## KIR3DL1 0 0 2 0 0 0
## KIR3DL2 0 0 1 1 1 1
## SMOX 611 312 247 227 746 279
## BMP2 168 499 277 454 350 508
## GINS1 173 126 106 123 270 176
## PLCG1 1788 1606 1558 1033 1425 1679
## YWHAB 9444 6278 6701 7668 7254 7858
## PI3 342 120 17 30 106419 346
## MMP9 381 94 77 114 337 148
## CD40 463 387 397 329 302 401
## PFDN4 503 437 504 337 450 321
## BMP7 814 684 662 1098 582 1257
## RPS21 17925 12471 15763 10576 9364 9772
## MX1 536 332 289 508 2032 525
## PFKL 5111 3159 3291 2846 2714 4184
## IL17RA 929 676 848 644 547 641
## UBE2L3 2332 1612 1580 1703 1884 1780
## TPST2 727 495 639 485 398 509
## XBP1 8865 2163 3157 1939 4072 2917
## UQCR10 1882 1012 991 834 1157 804
## SEC14L2 516 347 278 147 290 292
## APOL6 878 707 481 560 1405 663
## APOL1 448 248 164 271 1258 311
## TYMP 2256 800 642 519 8288 1256
## BEND2 1 0 0 0 0 0
## PHEX 23 25 18 13 26 22
## TIMP1 2316 1587 1127 1202 736 1708
## FOXP3 136 46 87 63 141 85
## MSN 7047 4740 4982 5900 5782 7038
## PGK1 11197 5120 6499 5975 10558 6766
## LAMP2 5886 3950 4258 4657 3680 4723
## CD40LG 42 25 30 29 30 37
## IRAK1 2992 1473 1828 1614 2250 1993
## COX2 45451 34142 29553 31046 16921 36381
## GSM5630002 GSM5630003 GSM5630004 GSM5630005 GSM5630006 GSM5630007
## TNFRSF9 230 16 18 21 1 17
## ENO1 25359 16143 19438 17046 9213 10302
## PIK3CD 433 134 242 151 29 226
## PGD 3054 4013 2906 3986 796 2362
## MTHFR 528 309 615 353 104 573
## TNFRSF1B 1266 379 524 395 41 482
## PINK1 1472 2190 1406 2191 509 1432
## IFNLR1 160 139 181 135 62 122
## RUNX3 986 386 369 471 38 437
## SH3BGRL3 5565 3263 3954 4054 2149 2478
## CD52 760 334 225 290 255 312
## IFI6 1632 741 20396 692 945 818
## ZC3H12A 2209 833 3854 844 55 402
## UTP11 1052 849 1103 911 1086 524
## JUN 1507 951 883 1311 236 1930
## KANK4 11 29 15 93 2 138
## EFCAB7 151 169 113 172 168 193
## IL23R 4 4 4 3 0 2
## ADGRL2 839 872 933 955 241 955
## GBP3 989 421 78 48 213 514
## GBP1 2836 524 2312 565 219 592
## GBP5 832 73 183 70 22 81
## TGFBR3 1372 1228 964 1436 632 1952
## VCAM1 834 412 272 235 58 463
## PTPN22 177 58 55 59 7 44
## CD160 18 5 7 5 3 17
## FCGR1A 114 19 68 14 9 8
## MCL1 6032 3314 4973 3905 1279 3523
## CTSK 11008 10150 8703 9642 7160 17688
## RORC 175 809 123 770 222 777
## S100A9 185832 18364 261474 4916 1151 1158
## S100A12 1038 18 1284 0 8 0
## S100A8 161703 18414 172864 4963 849 1566
## IL6R 687 392 443 422 127 340
## RIT1 770 641 730 638 454 464
## BGLAP 10 7 13 19 9 8
## IFI16 4295 1403 5924 1748 522 1664
## AIM2 153 19 150 15 17 11
## CRP 0 0 0 0 0 0
## FCGR2A 456 169 222 156 36 186
## HSPA6 151 64 318 118 34 75
## FCGR3A 625 96 316 63 34 61
## SELL 624 81 128 44 3 62
## GLUL 17580 9974 11792 9877 1938 7071
## PTGS2 65 27 67 34 25 22
## CRB1 6 3 4 5 0 3
## KDM5B 1563 1115 1502 1226 278 1271
## IL10 15 15 12 3 16 15
## YOD1 1458 624 4526 852 837 698
## HHAT 162 151 224 166 36 130
## TRAF5 199 81 107 103 38 250
## NLRP3 66 36 41 45 6 44
## LINC01250 0 0 0 0 0 0
## RPS7 27987 42228 23891 36201 92789 22950
## RSAD2 332 83 2013 88 41 115
## FOSL2 3136 2179 3889 2747 257 3180
## REL 1138 593 1136 590 191 648
## TGFA 1866 1052 1264 1102 417 615
## DYSF 345 171 211 210 15 238
## HK2 2099 1063 1997 1116 73 883
## CD8A 1103 205 218 141 62 127
## CD8B 410 99 64 69 54 108
## EIF5B 3482 2515 2335 2697 780 2446
## IL1R1 2106 1436 1503 1632 343 1645
## RGPD6 236 219 173 245 84 362
## IL1A 57 164 38 85 3 38
## IL1B 77 56 34 55 0 7
## IL37 298 1009 669 870 627 601
## IL36RN 7306 809 10911 1134 2884 918
## IL1F10 88 108 98 94 89 65
## IL1RN 2569 1148 1990 1404 429 754
## NMI 956 543 1429 567 702 440
## TNFAIP6 159 55 74 111 156 292
## IFIH1 683 272 1847 320 75 397
## SCN1A 1 2 19 10 4 10
## ABCB11 16 49 5 35 1 47
## RBM45 179 99 132 141 55 128
## FRZB 503 459 458 635 152 543
## TFPI 684 639 460 636 244 667
## STAT1 8183 1634 9468 1578 530 1753
## NABP1 335 140 372 161 39 257
## SF3B1 4896 3618 3680 3697 1405 4909
## CASP10 378 173 452 196 95 252
## CD28 286 49 96 51 9 72
## CTLA4 211 9 21 11 1 16
## ICOS 244 15 26 15 0 18
## NDUFS1 2229 3244 2349 3084 1376 1974
## CXCR2 317 131 398 117 15 69
## IRS1 194 298 274 507 42 610
## CCL20 57 11 54 19 1 9
## ATG16L1 453 445 393 486 225 458
## GPR35 21 7 22 12 0 20
## PDCD1 66 12 18 12 0 6
## PPARG 217 1110 311 955 43 656
## RPL15 41230 56761 35668 55263 72025 33322
## EOMES 82 12 25 9 1 13
## CX3CR1 153 151 127 135 9 101
## ACKR2 185 20 308 19 16 40
## CCR2 302 247 212 233 23 193
## CCRL2 34 17 26 17 13 11
## TLR9 20 5 6 6 1 6
## ARHGEF3 1078 580 1196 658 415 577
## ADAMTS9 132 75 51 137 7 185
## TMEM45A 16532 19942 18843 18662 13087 10053
## CD80 26 6 0 7 2 7
## CD86 347 167 137 176 57 127
## MIX23 284 231 246 273 503 219
## PLS1 150 65 133 135 36 114
## PTX3 28 23 28 21 4 25
## GOLIM4 993 574 701 913 316 1168
## MYNN 633 614 635 707 951 545
## TNFSF10 3219 1841 4202 2013 2263 1677
## ADIPOQ 1198 8 871 1235 9 1709
## SPON2 688 261 721 540 242 864
## S100P 1002 516 510 460 249 226
## WDR1 7641 5193 7038 5616 1628 4102
## CD38 42 5 28 5 0 20
## PPARGC1A 164 505 101 386 14 338
## TMPRSS11B 1 0 0 0 0 0
## CSN3 0 0 0 0 0 0
## ALB 0 2 0 1 0 7
## CXCL8 1094 5 84 2 21 1
## CXCL2 38 1 27 3 4 7
## AREG 152 154 50 165 67 75
## CXCL10 921 93 1049 9 26 64
## CXCL13 833 54 52 24 0 11
## SPP1 86 71 101 18 1 21
## HERC6 564 138 3147 144 59 166
## NFKB1 1677 707 1590 928 143 1019
## SEC24B 676 606 699 695 350 732
## EGF 18 42 10 53 6 66
## IL2 2 1 4 0 0 3
## IL21 24 0 2 0 0 1
## IL21-AS1 9 1 1 0 0 1
## SLC7A11 174 20 227 37 75 50
## IL15 154 94 58 77 48 90
## EDNRA 498 192 500 335 40 279
## TLR2 188 306 248 245 51 173
## FGB 0 0 0 0 0 0
## DDX60 888 334 2363 440 137 523
## SPCS3 2930 2446 2584 2949 2138 1730
## TLR3 191 69 172 100 43 97
## OSMR 791 336 736 444 76 492
## GZMK 333 63 62 35 3 66
## GZMA 349 86 149 53 35 60
## ANKRD55 17 8 3 2 4 11
## CENPK 158 117 164 121 295 71
## CAST 8393 6969 7516 7506 2464 5531
## ERAP1 2205 1567 1623 1498 369 1442
## ERAP2 1368 625 946 522 173 640
## TNFAIP8 1417 906 1191 1032 997 849
## CSF2 10 8 3 2 0 5
## IL5 0 0 0 3 0 1
## IL13 5 0 4 2 0 0
## IL4 0 0 0 0 2 2
## CD14 1198 692 901 810 344 791
## CSNK1A1 10737 6365 13457 7462 4094 5161
## PPARGC1B 296 198 222 224 8 250
## TNIP1 4366 2348 3403 2660 433 1962
## ATOX1 737 694 1003 670 1108 480
## FAXDC2 1047 2084 754 3136 78 1913
## IL12B 16 1 4 1 0 1
## MIR146A 3 1 1 3 0 1
## PDLIM7 595 476 557 440 91 719
## SERPINB1 3281 1055 1448 1063 1066 976
## SSR1 3339 2603 2457 2972 1024 2220
## CD83 596 137 189 180 39 161
## SOX4 957 656 660 835 204 987
## CMAHP 164 266 107 261 232 346
## HLA-A 33579 17569 21899 12262 4895 11303
## HLA-C 26644 13475 18695 11757 9006 13821
## HLA-B 49803 23702 33837 21639 5487 16587
## MICA 264 299 312 322 422 288
## LTA 29 10 13 12 0 11
## TNF 81 32 60 46 9 32
## HLA-DRB1 8692 4993 5016 5318 2243 5442
## HLA-DQB1 6802 3834 2873 2803 822 2674
## PPARD 1853 813 2912 983 127 793
## CCND3 1438 1015 1102 1191 256 850
## VEGFA 805 668 505 621 75 613
## RUNX2 132 54 67 42 13 87
## IL17A 17 2 3 1 0 0
## IL17F 10 0 13 1 0 0
## PRDM1 1341 387 1748 613 280 396
## ATG5 851 844 749 898 840 693
## TRAF3IP2 1056 740 1285 916 187 645
## NCOA7 1120 518 1067 855 198 738
## SGK1 3413 1387 3558 1737 837 1628
## IFNGR1 3690 3216 3903 2890 2415 2866
## TNFAIP3 750 285 440 381 56 521
## SOD2 9537 5849 8132 5163 1314 3933
## LPAL2 6 5 5 7 2 12
## PLG 2 5 1 1 0 1
## CCR6 151 122 47 112 8 118
## RAC1 7694 5452 7147 6229 3045 4453
## ZNF316 556 336 417 457 29 765
## AHR 2528 1820 3191 2632 1023 2672
## IL6 3 2 2 4 2 3
## TOMM7 4200 5859 3647 5548 3876 3427
## CYCS 5393 4586 4873 4213 4639 2513
## AQP1 8614 5630 7785 8782 7615 7282
## NT5C3A 2405 1111 3878 1106 916 616
## EGFR 2538 1906 2060 2344 386 4016
## CD36 6121 3514 9244 5034 2727 4291
## SAMD9 1179 181 2031 163 26 185
## SERPINE1 249 65 215 142 246 159
## CUX1 1230 696 1408 939 152 867
## PSMC2 3524 3200 3165 3238 2917 2250
## NAMPT 6259 3532 6829 2938 1378 2189
## HYAL4 105 38 280 14 9 27
## LEP 504 3 181 220 2 622
## CALD1 5372 5717 3829 4967 641 6091
## BRAF 442 366 379 405 95 539
## EZH2 501 238 588 318 244 237
## DNAJB6 5867 4424 8578 4526 2601 3070
## CSMD1 12 40 2 34 7 42
## CTSB 19174 12517 18909 14082 2972 8228
## EGR3 690 1230 977 1696 288 1113
## TNFRSF10A 215 117 212 130 97 131
## BNIP3L 5016 7297 6584 6134 3546 3893
## DUSP4 766 628 390 804 63 821
## NRG1 103 43 108 143 18 102
## RPL7 61235 85815 41034 77863 64301 45802
## IL7 119 117 188 167 108 206
## GEM 408 176 288 209 57 256
## MYC 1174 758 1394 969 253 801
## GPT 226 1638 134 1652 24 716
## JAK2 806 531 740 536 218 665
## CD274 239 30 166 24 3 27
## IL33 1685 1997 1746 1374 504 2233
## LURAP1L-AS1 2 2 4 3 3 10
## LURAP1L 447 393 492 385 252 351
## TTC39B 1254 3138 1768 2653 1108 1452
## IFNA1 0 0 0 0 0 0
## TEK 294 149 202 191 33 261
## TRBV20OR9-2 0 0 0 0 0 3
## TOMM5 1280 1416 1346 1402 2327 952
## ANXA1 15623 7287 5565 8887 9721 7883
## ERP44 1359 1120 1095 1084 828 896
## ZNF483 80 85 38 79 37 119
## TNFSF15 24 27 28 20 0 20
## TLR4 591 408 401 439 254 563
## PTGS1 2740 1339 2709 2436 1579 2408
## HSPA5 9140 7687 6576 7446 3219 6114
## FNBP1 1935 984 1260 1008 362 1275
## CARD9 101 62 79 54 18 98
## TRAF2 381 272 310 358 34 261
## CLIC3 1146 795 2295 1140 1056 596
## IL2RA 260 36 33 13 2 41
## GATA3 1068 1528 1095 2160 676 1888
## VIM 33881 17963 24638 29883 9564 28266
## CREM 422 418 337 370 355 361
## DKK1 38 39 10 37 4 57
## MBL2 3 0 0 0 0 0
## SAR1A 2276 2029 1955 2139 1961 2053
## PRF1 316 56 85 39 8 52
## ZMIZ1 1608 932 1152 1204 156 1785
## IFIT3 1374 446 4496 396 299 606
## TALDO1 4313 6772 4065 5338 3042 3098
## IGF2 245 137 230 180 26 309
## INS-IGF2 232 132 207 175 26 286
## IGF2-AS 1 0 0 0 0 2
## INS 0 0 0 0 0 0
## STIM1 1710 1021 1580 1467 248 1539
## TRIM22 1947 763 3736 778 209 1120
## PTH 0 0 0 0 0 0
## SAA1 361 1621 427 1011 13 648
## SLC1A2 49 17 5 42 14 49
## FOSL1 118 17 130 51 10 17
## CCND1 2666 2373 1139 3124 404 3832
## JRKL 139 91 105 122 24 156
## MMP7 382 862 314 295 534 214
## MMP1 265 19 355 12 0 17
## MMP3 86 12 40 1 6 6
## CASP1 1563 873 1717 1111 928 820
## DLAT 907 1048 970 1033 327 683
## IL18 2644 2979 2720 3242 4841 1726
## CD3E 933 214 211 184 8 191
## MCAM 2351 2291 2446 2400 350 2693
## WNK1 3116 2073 2707 2881 328 3643
## TNFRSF1A 3309 2393 3775 3246 741 2200
## GAPDH 43615 25767 39847 27820 16164 17886
## CD4 1476 602 799 675 70 819
## SLC2A3 538 202 273 293 55 223
## CLEC4D 12 3 2 1 0 2
## KLRB1 112 64 68 65 4 38
## CD69 197 99 64 63 32 93
## CLEC2B 1503 1216 1762 1411 6420 1363
## OLR1 9 6 3 1 0 6
## ABCD2 104 19 55 38 1 50
## LRRK2 289 206 299 228 65 427
## VDR 932 726 742 1105 93 890
## TMBIM6 13965 25043 12239 23803 4169 14114
## PFDN5 6923 9358 5232 8343 22142 6591
## SP7 5 0 0 0 0 2
## MUCL1 4611 963 2926 4752 1276 7520
## CD63 13672 12219 9775 14578 20649 14103
## SUOX 1648 2122 1462 2186 336 1607
## RPS26 5521 6811 10485 14391 23343 8617
## RPL41 73800 112705 59225 102306 170784 61802
## IL23A 16 3 26 7 0 3
## DDIT3 246 261 241 251 398 312
## IFNG 26 2 2 0 2 3
## IL22 3 1 1 2 0 0
## LYZ 8241 2841 4693 2553 1644 5479
## CAPS2 27 66 40 46 36 81
## SYT1 24 15 14 21 6 30
## BTG1 7202 6270 8441 5609 3638 4648
## HCAR3 425 149 306 140 57 179
## GJB2 22747 6326 34017 4358 965 1470
## GJB6 8023 3378 12124 2579 2796 853
## TNFSF11 29 32 15 23 1 17
## TPT1 134368 163998 112632 152793 308781 99656
## LMO7 1238 448 1481 692 207 737
## TNFSF13B 413 213 204 150 113 233
## LAMP1 4684 4067 4596 4753 576 4283
## PSME2 2866 1405 2245 1475 1588 1053
## GZMB 361 13 77 17 3 24
## RPL36AL 4603 4839 3879 5332 6574 3729
## PYGL 2166 1063 2425 1095 362 843
## HIF1A 2616 1172 2167 1316 364 1112
## FOS 462 616 233 546 490 645
## JDP2 763 525 806 894 390 996
## NOXRED1 8 10 15 12 2 17
## SERPINA1 407 146 392 178 2 134
## CDC42BPB 1966 1024 1771 1398 100 2022
## PLA2G4D 3761 482 7235 334 215 263
## TRIM69 42 7 22 22 11 21
## SLC51B 32 30 15 36 10 50
## SMAD3 874 718 763 580 150 984
## CYP1A1 6 30 1 192 22 223
## AKAP13 2123 1070 1070 1429 140 2351
## MEFV 73 2 26 3 0 10
## SOCS1 157 92 125 102 36 110
## ATXN2L 963 480 912 658 64 1074
## CD19 13 9 2 4 0 4
## ITGAL 561 95 148 66 2 176
## ITGAM 275 127 153 147 77 253
## ITGAX 147 48 76 69 4 193
## DNAJA2 2803 2533 3147 2682 2580 1875
## SIAH1 504 494 595 523 394 540
## ADCY7 823 335 710 443 100 991
## NOD2 680 298 1063 342 81 263
## CMTM2 3 6 2 1 0 5
## SF3B3 2497 2247 2557 2408 334 1831
## PSMD7 3056 3541 2960 3422 2891 2089
## WWOX 264 205 655 184 124 148
## MAF 3010 1983 3529 1997 1310 2605
## SLC7A5 1641 906 2666 1344 58 799
## CXCL16 1051 598 780 814 90 507
## XAF1 638 171 2630 213 57 540
## CD68 1875 677 1471 1001 372 725
## PER1 1913 1476 1040 1366 609 3142
## NOS2 184 1 15 1 3 5
## TRAF4 539 788 665 729 456 439
## CCL2 435 276 492 267 98 187
## CCL5 940 221 287 162 25 167
## CCL3 48 4 15 3 3 9
## CCR7 546 45 67 42 1 54
## STAT3 9586 3678 7312 4479 443 3892
## SOST 42 2 26 0 2 50
## ITGA2B 8 16 12 10 4 7
## EFCAB13 43 30 21 46 27 71
## NPEPPS 1965 1421 2044 1575 562 1610
## TBX21 41 8 9 3 0 6
## COL1A1 41821 16336 53945 11933 1384 81342
## MRPS23 1082 1093 998 1076 2374 774
## MIR21 1 0 1 1 0 2
## RPS6KB1 869 565 873 711 186 616
## ACE 449 93 457 192 23 581
## ERN1 415 189 398 250 38 374
## PRTN3 0 0 0 0 2 0
## PLIN5 238 1135 212 1053 0 744
## RETN 6 3 6 4 0 2
## CCL25 2 1 0 1 0 0
## ICAM1 713 294 411 271 67 369
## TYK2 742 362 741 561 49 1107
## SMARCA4 1542 889 1423 1212 109 1436
## ACP5 1890 1386 1844 1460 632 814
## JUNB 3374 2369 4450 2717 1167 1500
## CYP4F22 2362 2747 2886 2452 271 1550
## JAK3 322 56 83 55 1 138
## JUND 1623 1450 1794 2435 484 2326
## CEBPA 4449 5494 7163 6556 2218 4306
## CEBPG 1734 1512 2140 1823 1150 1338
## ACP7 2262 478 3879 348 89 269
## ZFP36 1154 538 1123 716 131 760
## TGFB1 1547 628 1290 945 102 756
## RPS19 31069 39151 29746 36693 53456 23963
## PSG2 6 0 0 0 0 0
## FUT2 354 73 348 96 83 48
## NKG7 293 44 116 32 17 61
## ZNF415 120 152 86 239 112 205
## LILRB2 185 44 83 58 21 65
## LILRA5 31 3 7 3 13 6
## KIR3DL1 0 2 0 0 0 0
## KIR3DL2 2 0 0 0 0 1
## SMOX 574 459 2009 532 154 221
## BMP2 388 217 182 346 196 342
## GINS1 286 198 283 185 304 120
## PLCG1 1321 640 1464 728 107 1164
## YWHAB 9901 7753 7842 8964 2964 6121
## PI3 35504 436 21874 197 14 66
## MMP9 836 118 476 48 1 180
## CD40 535 335 475 351 140 276
## PFDN4 525 589 380 538 1142 433
## BMP7 623 488 916 839 196 522
## RPS21 16181 23969 13612 20323 63640 13935
## MX1 1282 272 6384 279 27 332
## PFKL 2967 2993 2858 3449 287 2882
## IL17RA 754 490 655 610 62 572
## UBE2L3 2717 2516 2402 2585 1703 1580
## TPST2 718 501 802 558 173 621
## XBP1 4165 5352 4381 5699 2125 4642
## UQCR10 1485 2604 1344 1978 1383 1093
## SEC14L2 761 627 392 480 141 328
## APOL6 2007 398 2328 526 105 684
## APOL1 1537 219 1155 178 47 218
## TYMP 6919 1866 9099 1880 243 981
## BEND2 0 1 0 0 0 0
## PHEX 63 20 46 22 20 30
## TIMP1 1647 1239 1606 1293 462 1379
## FOXP3 371 54 68 64 6 63
## MSN 9232 3987 7045 5098 1298 4639
## PGK1 13261 11692 11805 10631 13761 6923
## LAMP2 4788 5219 4183 5063 2857 3800
## CD40LG 58 20 39 19 1 18
## IRAK1 2591 1628 2757 2075 578 1890
## COX2 22027 24262 11948 30015 5172 32235
## GSM5630008 GSM5630009 GSM5630010 GSM5630011 GSM5630012 GSM5630013
## TNFRSF9 42 3 14 49 96 15
## ENO1 24603 10657 26507 23486 19444 19681
## PIK3CD 602 263 293 330 538 409
## PGD 3216 996 2332 5330 3495 3141
## MTHFR 1653 676 456 576 987 798
## TNFRSF1B 1771 780 754 1045 1499 1097
## PINK1 2533 1974 2039 2617 1555 1786
## IFNLR1 419 210 196 161 306 297
## RUNX3 1729 661 1140 912 939 1740
## SH3BGRL3 4440 2940 4764 4682 4219 2773
## CD52 205 147 451 328 441 217
## IFI6 9931 1033 1326 1283 4883 875
## ZC3H12A 4628 420 692 1281 4524 584
## UTP11 1062 705 1036 764 918 693
## JUN 2604 1744 1690 1627 1247 1984
## KANK4 8 8 28 117 12 165
## EFCAB7 206 178 183 135 156 158
## IL23R 2 1 2 3 8 0
## ADGRL2 883 627 787 1030 610 807
## GBP3 476 187 447 309 721 563
## GBP1 1158 341 794 746 1229 868
## GBP5 442 109 81 139 346 160
## TGFBR3 6001 5285 2095 2117 1330 2762
## VCAM1 381 463 529 740 432 415
## PTPN22 93 44 69 82 105 54
## CD160 11 8 7 6 9 13
## FCGR1A 28 8 19 15 23 6
## MCL1 8276 5041 6012 5259 5777 4246
## CTSK 17967 31136 13559 16179 5013 6178
## RORC 449 604 604 954 181 827
## S100A9 90612 195 24021 16684 180837 2188
## S100A12 222 2 36 4 684 2
## S100A8 50725 70 20578 15384 182911 3636
## IL6R 1208 780 571 667 772 604
## RIT1 1050 589 777 727 764 515
## BGLAP 13 10 11 10 9 18
## IFI16 6316 1727 3057 2414 5352 2599
## AIM2 15 12 7 16 42 2
## CRP 1 0 0 0 0 0
## FCGR2A 298 242 165 100 146 78
## HSPA6 300 136 127 84 268 75
## FCGR3A 200 53 68 55 110 40
## SELL 86 38 184 174 255 82
## GLUL 37482 29717 11689 13840 10788 7016
## PTGS2 68 34 76 23 51 23
## CRB1 3 6 12 10 3 2
## KDM5B 3746 1421 1527 2046 2255 1604
## IL10 7 2 4 8 8 0
## YOD1 2941 1066 737 524 853 371
## HHAT 70 49 200 184 164 233
## TRAF5 299 276 206 171 310 259
## NLRP3 81 62 39 50 67 44
## LINC01250 0 0 2 3 0 0
## RPS7 22343 24347 32630 25630 23370 22586
## RSAD2 361 113 219 149 133 90
## FOSL2 11578 4575 4010 4887 4513 4826
## REL 3898 987 987 931 2181 1240
## TGFA 1661 709 779 927 609 356
## DYSF 415 231 361 376 386 509
## HK2 1994 420 735 1634 1161 873
## CD8A 326 151 165 166 256 75
## CD8B 109 64 126 76 88 27
## EIF5B 6744 3643 4060 3959 4663 5153
## IL1R1 4109 2307 2365 2152 1949 2159
## RGPD6 808 615 333 323 366 544
## IL1A 7 40 31 55 6 13
## IL1B 20 4 27 71 25 7
## IL37 687 1130 559 415 141 107
## IL36RN 5320 1404 2328 1018 5504 168
## IL1F10 273 218 109 54 62 14
## IL1RN 3542 1166 2249 2046 2318 932
## NMI 822 361 842 602 796 401
## TNFAIP6 81 247 148 115 98 51
## IFIH1 1181 289 618 448 814 483
## SCN1A 5 5 19 6 6 6
## ABCB11 9 10 14 67 31 52
## RBM45 219 149 227 140 202 203
## FRZB 612 260 1487 648 594 1654
## TFPI 1219 1249 739 805 491 540
## STAT1 9214 1948 3439 3163 6024 2938
## NABP1 154 97 228 180 311 223
## SF3B1 9661 6746 5021 4782 6632 6477
## CASP10 466 250 310 301 542 372
## CD28 146 24 86 111 192 56
## CTLA4 53 7 37 23 140 27
## ICOS 79 10 34 37 119 27
## NDUFS1 3295 2030 2541 3066 2168 2427
## CXCR2 266 45 125 198 455 73
## IRS1 613 448 402 647 293 672
## CCL20 145 0 76 77 121 2
## ATG16L1 1162 726 753 658 680 679
## GPR35 49 38 14 33 46 30
## PDCD1 52 16 18 34 57 17
## PPARG 161 153 181 779 101 431
## RPL15 31233 35905 50369 44002 34874 32851
## EOMES 88 33 17 34 45 17
## CX3CR1 228 82 161 232 198 134
## ACKR2 65 27 46 26 166 23
## CCR2 224 82 396 303 200 115
## CCRL2 18 17 32 49 14 18
## TLR9 17 3 5 7 19 7
## ARHGEF3 1451 796 1163 935 1040 674
## ADAMTS9 100 47 223 128 113 288
## TMEM45A 17184 7782 8554 16363 15071 3556
## CD80 4 0 5 7 19 3
## CD86 203 183 229 142 192 81
## MIX23 234 181 286 189 245 232
## PLS1 189 90 157 90 149 177
## PTX3 95 96 72 48 24 62
## GOLIM4 1442 1328 1355 1109 945 1504
## MYNN 705 616 682 619 522 498
## TNFSF10 4241 1527 3740 2247 3983 2359
## ADIPOQ 49 47 511 104 97 638
## SPON2 752 1036 931 785 847 968
## S100P 957 382 840 327 206 1194
## WDR1 7939 4665 7057 6378 6933 6013
## CD38 12 36 11 13 37 17
## PPARGC1A 215 183 452 333 88 1469
## TMPRSS11B 0 0 0 0 3 0
## CSN3 0 0 0 0 0 0
## ALB 0 3 3 1 2 9
## CXCL8 14 1 1 1 63 2
## CXCL2 15 10 7 3 31 9
## AREG 85 24 200 136 99 97
## CXCL10 231 20 25 57 153 110
## CXCL13 76 0 2 127 100 13
## SPP1 6 19 8 77 88 158
## HERC6 3064 169 410 279 926 373
## NFKB1 3047 1248 1627 1618 2038 1417
## SEC24B 1600 971 821 861 1024 1025
## EGF 12 13 71 39 15 108
## IL2 0 0 3 1 1 0
## IL21 1 0 0 1 7 0
## IL21-AS1 0 0 0 4 2 1
## SLC7A11 238 25 43 45 262 27
## IL15 114 100 66 74 142 146
## EDNRA 402 250 594 417 489 583
## TLR2 415 176 158 386 240 175
## FGB 0 0 0 0 0 0
## DDX60 1938 482 721 621 1123 431
## SPCS3 3074 2490 2655 2478 2399 1903
## TLR3 296 142 212 189 202 159
## OSMR 1785 761 810 853 1019 695
## GZMK 106 44 54 87 61 25
## GZMA 119 46 122 79 95 53
## ANKRD55 11 9 20 10 22 7
## CENPK 49 24 126 68 154 91
## CAST 16588 9858 10113 8751 6422 7914
## ERAP1 4214 1935 2794 2448 2095 2448
## ERAP2 203 88 108 95 2737 2142
## TNFAIP8 2070 1122 1184 1234 1393 836
## CSF2 1 0 3 5 4 0
## IL5 0 2 1 2 1 6
## IL13 4 0 14 0 0 1
## IL4 1 0 0 1 0 1
## CD14 1160 1332 838 735 621 385
## CSNK1A1 13457 6325 9417 7372 10302 5621
## PPARGC1B 1098 215 228 425 717 301
## TNIP1 5472 2426 3271 3362 3121 2381
## ATOX1 809 460 728 533 820 446
## FAXDC2 2491 1830 942 3396 1128 1362
## IL12B 0 0 0 4 13 1
## MIR146A 3 1 1 1 9 1
## PDLIM7 737 785 928 527 912 1159
## SERPINB1 1325 1146 1697 1188 1262 1064
## SSR1 3715 2698 3306 3344 5029 4737
## CD83 241 43 324 412 523 288
## SOX4 1657 1509 1244 1299 1330 2669
## CMAHP 299 331 485 335 155 410
## HLA-A 44862 21463 25485 24201 30997 28521
## HLA-C 42326 20340 30776 32128 27761 23932
## HLA-B 62519 28264 34087 33947 39711 33033
## MICA 386 315 343 247 404 390
## LTA 25 12 21 17 18 25
## TNF 76 29 92 77 108 46
## HLA-DRB1 6781 4368 11731 10914 3354 2278
## HLA-DQB1 656 438 5184 5171 882 587
## PPARD 3843 876 1153 1481 1929 889
## CCND3 2519 1440 1267 1385 1201 1015
## VEGFA 812 497 1014 949 657 1296
## RUNX2 201 150 90 102 98 134
## IL17A 5 0 0 1 22 0
## IL17F 1 0 1 1 26 0
## PRDM1 2462 575 497 656 1166 223
## ATG5 854 814 971 783 777 671
## TRAF3IP2 1737 860 1193 1108 1300 1027
## NCOA7 1684 974 873 878 1168 749
## SGK1 4214 1042 3039 1609 3658 2839
## IFNGR1 4213 3538 3394 3007 3353 2511
## TNFAIP3 927 291 582 659 809 550
## SOD2 9888 5608 7769 7568 7439 5832
## LPAL2 8 4 6 6 16 12
## PLG 1 2 1 3 0 1
## CCR6 165 54 160 156 130 88
## RAC1 11692 6961 8704 8121 9539 6780
## ZNF316 1718 1018 732 735 1399 1610
## AHR 3398 1934 2912 2698 2994 2358
## IL6 3 1 7 2 4 7
## TOMM7 3016 4233 3920 3577 4201 4728
## CYCS 3218 1580 4088 3654 3544 2662
## AQP1 14840 14149 17430 16157 10196 16347
## NT5C3A 2449 910 923 878 1549 482
## EGFR 12766 6152 4354 4817 4578 7215
## CD36 1960 943 1449 4175 3574 1929
## SAMD9 983 166 250 244 640 222
## SERPINE1 485 233 154 72 177 86
## CUX1 2258 1149 1678 1327 1530 1459
## PSMC2 4356 2866 3413 3265 2973 1998
## NAMPT 5897 2502 3094 3002 3877 2533
## HYAL4 118 7 54 33 178 119
## LEP 35 16 138 25 63 246
## CALD1 9070 9366 9526 6526 4952 7583
## BRAF 1161 661 532 542 620 658
## EZH2 949 313 418 351 1018 489
## DNAJB6 5793 3343 4937 4245 4573 2493
## CSMD1 5 26 58 44 15 38
## CTSB 15544 10628 16179 19230 12912 9225
## EGR3 1859 580 747 1779 706 567
## TNFRSF10A 349 166 170 137 193 170
## BNIP3L 8822 5237 4080 5994 3152 2466
## DUSP4 472 340 419 922 490 994
## NRG1 191 61 171 134 103 257
## RPL7 58290 62451 83852 71876 47545 47803
## IL7 181 131 154 150 180 77
## GEM 311 139 458 231 439 233
## MYC 2937 1006 1400 1253 1838 918
## GPT 182 124 205 1935 335 461
## JAK2 1779 1186 830 831 934 621
## CD274 151 38 66 63 168 57
## IL33 922 834 2513 1971 1668 1538
## LURAP1L-AS1 7 6 1 3 3 0
## LURAP1L 496 281 1095 429 477 555
## TTC39B 1693 665 817 2306 1405 1012
## IFNA1 0 0 0 0 0 0
## TEK 378 219 386 388 230 390
## TRBV20OR9-2 2 0 0 0 0 0
## TOMM5 1064 864 1172 1065 983 745
## ANXA1 7563 8175 8970 6531 4038 6419
## ERP44 1621 1198 1260 1150 1256 947
## ZNF483 163 194 77 77 95 147
## TNFSF15 26 7 38 58 36 63
## TLR4 1168 1631 520 397 207 227
## PTGS1 9252 4907 3767 3416 3681 2526
## HSPA5 11463 5816 8914 11397 8407 8102
## FNBP1 3198 1852 1650 1486 2267 1608
## CARD9 144 53 139 119 204 92
## TRAF2 527 199 470 478 629 513
## CLIC3 930 737 816 930 1940 454
## IL2RA 142 46 48 42 131 49
## GATA3 5013 2567 2770 3282 2653 3820
## VIM 44629 52531 36161 34352 23275 31418
## CREM 385 348 447 375 292 308
## DKK1 477 746 68 24 15 83
## MBL2 0 0 0 0 3 0
## SAR1A 2633 2447 2526 2336 2175 2351
## PRF1 154 68 65 62 88 51
## ZMIZ1 4546 2184 1683 2028 2309 2711
## IFIT3 1988 576 986 710 1276 636
## TALDO1 3255 1824 3365 4935 3987 2855
## IGF2 442 285 447 465 557 823
## INS-IGF2 424 260 416 432 502 764
## IGF2-AS 2 3 1 0 2 4
## INS 1 0 0 0 0 1
## STIM1 3430 2203 2749 2056 1515 2427
## TRIM22 2515 857 1136 886 1688 871
## PTH 0 0 0 0 0 0
## SAA1 222 20 64 816 184 1319
## SLC1A2 24 40 31 73 53 59
## FOSL1 138 21 240 23 68 14
## CCND1 3935 3073 4002 5262 4385 7342
## JRKL 266 144 241 221 203 311
## MMP7 293 306 872 295 206 1739
## MMP1 0 3 28 126 52 14
## MMP3 737 796 25 19 2 39
## CASP1 873 438 1332 846 1509 829
## DLAT 1397 792 1042 1186 798 853
## IL18 2650 1830 2753 2038 2162 1234
## CD3E 363 87 279 385 514 147
## MCAM 3942 2301 3533 3301 2844 4568
## WNK1 14544 6925 4165 5345 4269 4215
## TNFRSF1A 5769 2800 4193 4080 4257 3074
## GAPDH 36975 21142 45621 30895 36523 37010
## CD4 1460 1197 1346 1249 1260 678
## SLC2A3 604 376 354 378 361 409
## CLEC4D 4 0 1 0 3 0
## KLRB1 111 32 70 74 124 40
## CD69 79 82 91 60 79 44
## CLEC2B 2290 2169 2535 1425 2558 1759
## OLR1 3 0 1 10 3 1
## ABCD2 33 39 61 29 29 41
## LRRK2 465 328 230 224 292 264
## VDR 1922 728 1042 1681 1388 1593
## TMBIM6 15703 11145 15825 26047 10567 13729
## PFDN5 5090 6115 8176 6156 5424 6974
## SP7 1 2 0 0 2 7
## MUCL1 3160 57 13093 585 1544 11171
## CD63 9913 15661 17474 15235 9133 11394
## SUOX 2459 1769 2072 2607 1804 1891
## RPS26 8467 8071 11817 10976 4853 3795
## RPL41 48701 63320 87854 76925 62312 58712
## IL23A 26 9 10 11 24 8
## DDIT3 495 436 361 259 318 501
## IFNG 3 1 3 1 12 8
## IL22 0 0 1 0 3 1
## LYZ 3519 1259 6494 5402 8949 1358
## CAPS2 41 77 55 59 30 101
## SYT1 14 62 27 30 9 38
## BTG1 13303 7551 8256 7622 8856 6644
## HCAR3 484 126 322 296 626 174
## GJB2 7454 670 6227 7392 16776 4027
## GJB6 4210 671 3764 2647 5662 2906
## TNFSF11 8 5 10 12 13 11
## TPT1 107918 110121 155897 118691 106412 79430
## LMO7 2244 651 1427 873 1323 747
## TNFSF13B 146 109 220 265 323 193
## LAMP1 11336 7239 6031 8660 6160 7271
## PSME2 1915 853 2142 1605 2097 1563
## GZMB 65 19 52 36 127 10
## RPL36AL 3949 3547 5738 4277 4084 3339
## PYGL 2059 869 1789 1152 1996 1225
## HIF1A 3001 1482 2558 1588 2058 2928
## FOS 616 396 764 420 255 301
## JDP2 1289 1199 1095 875 843 728
## NOXRED1 50 24 19 13 14 17
## SERPINA1 194 53 158 210 218 90
## CDC42BPB 4540 2167 2349 2377 2627 2482
## PLA2G4D 3002 208 541 612 3557 89
## TRIM69 26 6 17 12 22 14
## SLC51B 44 50 40 37 11 41
## SMAD3 3022 1348 1139 1162 1526 1366
## CYP1A1 140 46 54 24 19 8
## AKAP13 5721 3266 2217 2317 2341 2839
## MEFV 37 22 12 19 23 12
## SOCS1 149 167 112 123 175 145
## ATXN2L 2061 1077 966 1056 1737 1471
## CD19 8 0 2 8 15 13
## ITGAL 348 96 194 212 552 214
## ITGAM 278 381 265 205 230 162
## ITGAX 61 52 131 78 254 213
## DNAJA2 3684 2546 2872 2415 2394 1770
## SIAH1 776 633 605 483 666 750
## ADCY7 1114 882 752 743 1192 745
## NOD2 1439 260 411 351 1144 284
## CMTM2 1 1 0 15 11 5
## SF3B3 3540 1843 2824 3209 3157 2193
## PSMD7 4614 2812 3060 3364 2915 2348
## WWOX 1405 329 203 240 263 224
## MAF 6519 4198 3750 3277 4723 3940
## SLC7A5 5074 674 640 1772 4091 2232
## CXCL16 1070 553 835 954 1037 789
## XAF1 2627 373 432 298 1559 679
## CD68 1448 1829 980 861 770 374
## PER1 5181 4219 2553 2063 1554 2771
## NOS2 30 8 15 15 92 8
## TRAF4 670 304 835 575 656 948
## CCL2 129 47 561 386 458 306
## CCL5 530 192 231 319 236 104
## CCL3 17 9 2 10 21 4
## CCR7 153 17 161 403 367 138
## STAT3 11469 3969 7473 7098 8628 6877
## SOST 0 0 348 5 12 14
## ITGA2B 6 10 15 4 11 17
## EFCAB13 57 64 34 39 48 73
## NPEPPS 2421 1490 1871 1756 2159 1606
## TBX21 25 10 16 17 21 13
## COL1A1 31919 16479 72813 106105 57213 90373
## MRPS23 1094 1022 1058 834 877 781
## MIR21 2 0 1 1 1 1
## RPS6KB1 1848 1182 903 877 915 865
## ACE 455 291 493 487 710 950
## ERN1 1056 491 504 503 652 569
## PRTN3 0 0 0 1 0 4
## PLIN5 383 20 52 1263 493 465
## RETN 3 4 12 5 4 0
## CCL25 1 0 5 0 0 0
## ICAM1 1394 965 570 599 744 636
## TYK2 1723 924 802 743 1723 1408
## SMARCA4 4331 1358 1779 2043 3099 3312
## ACP5 1775 893 822 1627 1387 605
## JUNB 4805 1535 3885 3439 4400 2320
## CYP4F22 4247 997 1028 4441 2480 561
## JAK3 192 61 90 109 398 177
## JUND 3736 2241 2240 2543 2556 3204
## CEBPA 11518 3779 4164 6264 7960 3951
## CEBPG 2969 1536 2167 1735 1801 1588
## ACP7 2834 130 175 400 1448 44
## ZFP36 2146 965 1131 841 1194 822
## TGFB1 2342 1341 1307 1345 1760 1228
## RPS19 30384 26587 39467 30946 31227 26552
## PSG2 9 3 1 0 0 0
## FUT2 301 50 139 101 169 106
## NKG7 79 31 63 69 86 52
## ZNF415 141 133 205 165 54 144
## LILRB2 85 93 69 58 117 46
## LILRA5 1 8 8 1 5 1
## KIR3DL1 0 0 0 0 2 0
## KIR3DL2 1 2 2 0 0 1
## SMOX 1706 354 354 841 569 165
## BMP2 420 447 358 289 243 378
## GINS1 225 130 234 195 311 193
## PLCG1 3353 1761 1318 1392 2148 1731
## YWHAB 11194 7113 10369 11285 8199 7731
## PI3 8180 98 1229 691 25963 36
## MMP9 165 69 146 343 702 181
## CD40 379 222 574 406 586 564
## PFDN4 446 402 655 428 502 381
## BMP7 1716 756 659 836 1112 784
## RPS21 8975 12680 16714 14178 15627 18254
## MX1 4966 542 1051 711 1601 725
## PFKL 4385 2831 3619 4979 4229 4143
## IL17RA 1600 885 659 919 948 811
## UBE2L3 3003 1656 2552 2549 2373 1882
## TPST2 1011 499 755 681 788 522
## XBP1 4556 2557 5568 4479 3099 4875
## UQCR10 1026 849 1280 1583 1199 978
## SEC14L2 836 187 302 563 305 387
## APOL6 2107 555 855 872 1728 949
## APOL1 479 147 414 383 878 603
## TYMP 5229 478 2179 2895 7053 2045
## BEND2 0 0 0 0 1 1
## PHEX 23 38 33 16 41 25
## TIMP1 1187 1969 2162 2002 1075 1472
## FOXP3 256 36 69 71 338 108
## MSN 10739 6073 10658 7946 7630 6631
## PGK1 10445 7217 12537 11370 9508 7211
## LAMP2 6370 4848 5701 6551 4188 4421
## CD40LG 31 8 54 68 56 32
## IRAK1 4936 2088 2447 2907 2888 2483
## COX2 42486 28818 33164 40947 54631 73749
## GSM5630014 GSM5630015 GSM5630016 GSM5630017 GSM5630018 GSM5630019
## TNFRSF9 259 20 38 6 122 12
## ENO1 47473 17112 24119 14453 25604 16717
## PIK3CD 830 562 186 286 225 161
## PGD 6906 3407 2270 2134 3525 1970
## MTHFR 1282 1080 364 538 775 327
## TNFRSF1B 3013 1109 830 734 831 364
## PINK1 2632 2795 758 2476 1319 1606
## IFNLR1 554 253 91 133 238 69
## RUNX3 1304 1056 376 546 463 380
## SH3BGRL3 14766 4826 4716 6161 6005 3239
## CD52 1664 467 616 353 326 243
## IFI6 39656 1333 9966 18562 17207 1059
## ZC3H12A 12568 694 1429 221 8014 410
## UTP11 3555 1133 1577 1164 1978 928
## JUN 2656 2018 618 1169 1620 1288
## KANK4 24 131 2 148 10 11
## EFCAB7 382 340 138 180 138 189
## IL23R 27 5 6 0 9 0
## ADGRL2 2339 1182 444 799 1518 514
## GBP3 348 213 799 1175 1147 399
## GBP1 6544 687 2214 1266 4668 588
## GBP5 1701 201 169 203 483 38
## TGFBR3 1347 3670 1148 2826 861 1339
## VCAM1 1233 488 477 473 441 463
## PTPN22 526 112 68 81 182 21
## CD160 25 37 12 9 4 10
## FCGR1A 209 7 216 19 120 22
## MCL1 16651 6543 3659 5066 9750 3308
## CTSK 20778 30584 7645 37129 23262 16584
## RORC 197 1039 53 578 89 349
## S100A9 1539675 800 248022 1496 542849 7732
## S100A12 8999 0 873 0 5233 3
## S100A8 1411779 916 263285 288 466727 6388
## IL6R 1532 937 438 654 667 292
## RIT1 2853 968 678 767 1056 555
## BGLAP 30 32 18 11 17 15
## IFI16 13183 3350 4567 4025 7480 1815
## AIM2 304 32 58 47 216 6
## CRP 0 1 2 0 2 0
## FCGR2A 1305 386 569 387 662 234
## HSPA6 625 166 192 130 225 156
## FCGR3A 1465 83 467 154 765 91
## SELL 1075 64 249 90 195 34
## GLUL 52821 13668 7784 13393 29472 8995
## PTGS2 1751 29 22 52 122 92
## CRB1 0 9 4 6 0 0
## KDM5B 4137 2585 826 1426 2206 864
## IL10 139 54 20 52 50 13
## YOD1 5015 1137 1482 1367 3666 739
## HHAT 229 198 85 141 145 111
## TRAF5 356 647 199 272 170 167
## NLRP3 193 88 73 98 69 33
## LINC01250 1 0 0 0 0 1
## RPS7 124264 57413 38700 39422 36035 45195
## RSAD2 2620 160 646 1348 2985 157
## FOSL2 8198 4449 1584 2585 4218 2010
## REL 3381 1508 901 806 1503 434
## TGFA 7442 918 592 832 4981 543
## DYSF 619 408 270 238 324 274
## HK2 5319 1305 786 603 3282 435
## CD8A 1125 154 135 93 228 88
## CD8B 396 68 70 66 115 104
## EIF5B 6629 5031 4472 5424 3888 2747
## IL1R1 4656 3913 1697 2426 2897 1432
## RGPD6 505 734 206 412 308 238
## IL1A 294 44 4 87 298 65
## IL1B 3780 35 12 25 669 9
## IL37 502 1396 256 2105 214 1023
## IL36RN 38542 942 5356 2118 31989 1934
## IL1F10 94 71 81 201 134 218
## IL1RN 13352 1481 1235 1503 7690 1101
## NMI 4680 898 1540 1321 2527 879
## TNFAIP6 759 282 145 322 285 119
## IFIH1 3153 684 979 1343 2143 367
## SCN1A 13 15 3 6 1 1
## ABCB11 55 99 1 35 6 5
## RBM45 359 228 189 171 257 128
## FRZB 387 704 364 918 116 700
## TFPI 1145 1444 454 1387 720 618
## STAT1 16232 2932 4601 7495 11793 1698
## NABP1 1913 413 251 254 864 143
## SF3B1 11847 11322 3746 5127 5304 2985
## CASP10 1492 615 281 400 809 179
## CD28 504 104 190 66 117 38
## CTLA4 298 50 58 9 96 7
## ICOS 347 52 55 17 81 3
## NDUFS1 6604 4819 2020 2111 3492 1691
## CXCR2 1279 122 305 103 573 119
## IRS1 561 962 177 675 304 446
## CCL20 527 13 58 2 330 28
## ATG16L1 1343 1139 439 510 645 425
## GPR35 72 60 38 14 26 25
## PDCD1 220 24 19 6 52 7
## PPARG 282 908 106 564 183 220
## RPL15 127223 71375 39722 54064 41499 63711
## EOMES 137 62 23 10 24 10
## CX3CR1 376 214 159 225 115 41
## ACKR2 609 67 129 34 613 39
## CCR2 1139 369 312 341 293 189
## CCRL2 273 39 28 37 45 16
## TLR9 59 17 4 7 16 7
## ARHGEF3 2464 1206 907 1103 1229 749
## ADAMTS9 283 189 186 209 186 115
## TMEM45A 55388 13697 14895 11875 24181 9615
## CD80 134 12 11 3 40 3
## CD86 871 300 233 387 339 140
## MIX23 1351 397 701 397 565 304
## PLS1 489 170 148 119 296 127
## PTX3 101 105 307 35 89 31
## GOLIM4 1522 2023 783 2080 1081 1122
## MYNN 2368 1086 699 856 976 707
## TNFSF10 19223 3746 2545 3701 6042 2651
## ADIPOQ 202 552 272 3909 72 667
## SPON2 463 1513 246 1014 648 843
## S100P 12849 1956 617 351 4472 1286
## WDR1 14165 6206 6862 5606 9039 5836
## CD38 154 18 33 8 74 5
## PPARGC1A 105 845 8 207 54 234
## TMPRSS11B 0 0 0 0 2 0
## CSN3 0 0 0 0 0 0
## ALB 0 9 5 0 0 0
## CXCL8 88601 6 10 12 5286 1
## CXCL2 427 14 17 4 169 25
## AREG 1052 129 125 47 574 45
## CXCL10 2211 45 275 32 644 66
## CXCL13 1077 1 69 1 324 0
## SPP1 1152 178 5 276 73 16
## HERC6 4114 295 930 1562 3529 167
## NFKB1 4476 1870 1147 1330 1936 887
## SEC24B 1692 1495 609 875 714 441
## EGF 8 121 4 75 18 39
## IL2 7 3 2 0 6 0
## IL21 38 2 2 0 20 0
## IL21-AS1 12 0 0 0 3 0
## SLC7A11 717 28 165 65 352 43
## IL15 375 165 123 125 114 68
## EDNRA 615 412 264 497 495 398
## TLR2 1293 470 226 170 346 77
## FGB 0 0 0 0 0 0
## DDX60 4396 1080 1273 2210 2982 333
## SPCS3 10404 3310 3735 3392 4928 2648
## TLR3 394 243 138 285 187 114
## OSMR 2458 1004 760 564 1446 390
## GZMK 575 136 90 49 61 26
## GZMA 1054 186 184 98 290 73
## ANKRD55 17 15 3 8 8 5
## CENPK 765 131 338 99 277 106
## CAST 16513 11062 5843 10730 7214 6191
## ERAP1 4575 3441 1279 2441 1898 1461
## ERAP2 2951 1277 125 143 142 49
## TNFAIP8 4877 1392 1134 1503 1363 902
## CSF2 39 9 1 3 17 0
## IL5 0 13 2 0 0 0
## IL13 12 1 1 1 8 1
## IL4 7 3 1 1 0 1
## CD14 3485 1656 1753 1328 1596 1099
## CSNK1A1 28456 8025 10987 8753 18434 6992
## PPARGC1B 800 358 234 136 304 91
## TNIP1 9676 2949 2047 3500 5478 2179
## ATOX1 3719 934 1041 915 1427 667
## FAXDC2 2428 3860 427 1783 758 695
## IL12B 96 1 6 0 147 0
## MIR146A 9 5 1 1 7 0
## PDLIM7 1501 898 601 714 715 1584
## SERPINB1 19259 1862 1819 2160 12501 1260
## SSR1 8448 3630 2966 3626 4570 2154
## CD83 1591 216 289 179 438 163
## SOX4 1382 1619 522 1004 584 750
## CMAHP 303 902 180 641 197 268
## HLA-A 67374 23278 24796 28564 28643 19757
## HLA-C 81016 27748 21600 32602 26871 18181
## HLA-B 114699 31894 44690 48593 47652 27234
## MICA 835 792 291 444 337 282
## LTA 41 31 15 14 7 10
## TNF 287 73 63 43 92 30
## HLA-DRB1 10305 3516 5678 7006 8364 5875
## HLA-DQB1 2522 626 3374 4505 4303 2780
## PPARD 5478 1429 871 929 3580 606
## CCND3 3164 1512 1093 1282 1540 807
## VEGFA 2161 1681 307 484 1086 393
## RUNX2 215 129 48 66 72 38
## IL17A 242 0 6 0 63 0
## IL17F 133 0 8 0 64 0
## PRDM1 5201 672 814 489 2583 253
## ATG5 2819 1383 1092 1304 1197 934
## TRAF3IP2 2382 1093 810 1134 1435 802
## NCOA7 4586 1350 953 923 2458 556
## SGK1 13883 2990 2257 1713 5046 1736
## IFNGR1 21029 4469 3910 4083 8149 3100
## TNFAIP3 1789 601 325 378 581 312
## SOD2 41117 8213 7370 5621 20906 5147
## LPAL2 21 19 4 2 9 3
## PLG 0 4 1 3 4 0
## CCR6 360 148 73 140 138 49
## RAC1 19180 7896 7875 9349 9838 5648
## ZNF316 1464 1589 416 870 534 462
## AHR 8196 4095 3821 3132 3294 1731
## IL6 23 4 13 1 23 11
## TOMM7 10434 8953 4319 6175 3541 4851
## CYCS 24315 5152 9080 3956 11212 4036
## AQP1 11551 14760 6569 14375 8720 12557
## NT5C3A 11971 1345 2210 1550 5362 948
## EGFR 5238 7785 1503 3331 2216 1531
## CD36 9082 5258 3166 5365 9025 2489
## SAMD9 4165 289 540 489 3049 169
## SERPINE1 181 80 374 137 390 118
## CUX1 2887 1639 723 1499 1521 1043
## PSMC2 10176 3716 4070 4300 5046 3046
## NAMPT 43858 3569 5676 2879 28428 2373
## HYAL4 1155 39 94 19 462 7
## LEP 59 176 48 1318 1 19
## CALD1 7401 10280 5699 12328 4981 9887
## BRAF 870 1089 266 711 339 318
## EZH2 1770 653 480 324 762 247
## DNAJB6 20576 4756 4637 4678 11742 4278
## CSMD1 51 70 13 30 0 17
## CTSB 49053 13886 15502 17136 25686 9168
## EGR3 2251 1245 533 813 1221 570
## TNFRSF10A 768 250 228 138 440 114
## BNIP3L 13939 6964 3065 5224 7469 3011
## DUSP4 890 959 216 711 402 369
## NRG1 367 237 213 183 175 80
## RPL7 144299 101431 51753 76548 58194 83665
## IL7 430 246 221 303 222 161
## GEM 533 382 185 277 459 357
## MYC 4405 1101 1340 785 2873 924
## GPT 298 1276 63 262 80 274
## JAK2 2030 1273 538 974 786 477
## CD274 1076 80 78 72 903 22
## IL33 8727 3917 2198 2576 2482 2069
## LURAP1L-AS1 16 9 4 13 2 1
## LURAP1L 1153 607 421 553 507 558
## TTC39B 5150 2522 1071 1109 2671 688
## IFNA1 0 0 0 0 0 0
## TEK 450 568 155 232 262 208
## TRBV20OR9-2 0 1 0 0 0 2
## TOMM5 6087 2072 2827 1972 2325 1561
## ANXA1 66900 13912 9473 19378 36947 8879
## ERP44 3562 1654 1428 1659 1769 1106
## ZNF483 65 299 84 146 33 82
## TNFSF15 144 51 19 16 69 29
## TLR4 1283 1371 525 1437 787 594
## PTGS1 4718 4963 1984 4020 2396 2437
## HSPA5 26996 9439 11984 7461 17800 6077
## FNBP1 3456 2569 1196 1903 1679 1373
## CARD9 348 169 77 87 149 85
## TRAF2 906 426 207 248 308 255
## CLIC3 3552 813 977 1197 1721 1360
## IL2RA 331 65 73 37 105 20
## GATA3 2300 3238 1140 2844 616 1128
## VIM 34118 51201 24339 66123 21959 34265
## CREM 1553 668 572 610 638 485
## DKK1 9 77 54 85 93 54
## MBL2 0 0 0 0 0 3
## SAR1A 6632 4367 2506 3819 3462 2340
## PRF1 547 67 77 58 215 41
## ZMIZ1 3103 3194 748 1665 1381 1045
## IFIT3 5532 820 2363 4654 4682 803
## TALDO1 12665 4935 3951 4098 5842 3279
## IGF2 532 464 166 345 148 262
## INS-IGF2 509 423 154 322 133 246
## IGF2-AS 1 2 0 1 3 0
## INS 0 0 0 0 0 0
## STIM1 2869 2899 988 1980 1671 1276
## TRIM22 7213 2105 1525 2635 5002 674
## PTH 0 0 0 0 0 0
## SAA1 1868 464 142 734 488 62
## SLC1A2 77 150 12 41 85 32
## FOSL1 400 11 144 27 531 22
## CCND1 2976 6881 1845 5839 1374 2697
## JRKL 302 497 87 199 151 98
## MMP7 432 2092 112 462 255 413
## MMP1 219 8 77 11 118 8
## MMP3 91 10 15 11 16 20
## CASP1 5460 1309 2198 2047 2992 1495
## DLAT 2063 1257 1032 806 1371 796
## IL18 6224 4310 3318 5509 1530 2425
## CD3E 1138 239 295 121 277 86
## MCAM 3047 2551 2259 2578 1738 4082
## WNK1 5660 7301 1660 4825 2586 2239
## TNFRSF1A 9108 3515 2554 3001 4922 2681
## GAPDH 82652 26742 41128 27718 43730 36899
## CD4 2651 1466 944 1726 1337 791
## SLC2A3 2511 409 283 339 337 183
## CLEC4D 63 0 5 2 13 4
## KLRB1 338 100 60 25 107 43
## CD69 552 174 108 76 140 91
## CLEC2B 7694 3507 2567 4078 2160 2772
## OLR1 148 3 3 4 15 3
## ABCD2 76 87 9 102 32 33
## LRRK2 719 653 166 514 299 290
## VDR 2117 1598 587 1008 1001 427
## TMBIM6 34276 21936 11034 17497 18560 12756
## PFDN5 26329 16453 8669 12312 7656 11325
## SP7 0 6 0 0 0 1
## MUCL1 3803 8236 177 3392 1061 5434
## CD63 32632 23661 16206 30551 16505 20120
## SUOX 3155 2627 1042 2265 1585 1429
## RPS26 47689 18510 10876 5972 15060 14300
## RPL41 293694 152143 103169 130723 87204 115565
## IL23A 152 12 13 7 76 9
## DDIT3 1085 895 375 573 262 397
## IFNG 77 5 7 0 30 0
## IL22 30 0 1 0 71 0
## LYZ 28776 3311 6912 4478 14828 5151
## CAPS2 77 178 34 94 23 61
## SYT1 12 54 12 61 1 16
## BTG1 19550 7064 4875 7563 6565 5930
## HCAR3 4787 241 154 288 904 134
## GJB2 122883 2091 11571 1004 80353 2078
## GJB6 31307 1372 4802 1168 22654 1763
## TNFSF11 30 12 10 4 4 2
## TPT1 553032 225783 138123 144320 183432 199137
## LMO7 2711 1450 862 1057 2500 645
## TNFSF13B 876 347 409 476 369 226
## LAMP1 8929 7387 2968 5894 4499 3180
## PSME2 9157 1813 3590 2685 3750 1544
## GZMB 1284 27 96 22 654 15
## RPL36AL 16796 7350 6433 8092 6700 4027
## PYGL 6087 1631 1871 1833 3763 998
## HIF1A 6254 2631 1779 1516 4515 1412
## FOS 1196 522 469 272 869 390
## JDP2 1208 1592 737 1612 801 1025
## NOXRED1 37 58 11 19 13 9
## SERPINA1 1930 88 293 112 1050 212
## CDC42BPB 4020 3635 1007 2549 2189 1315
## PLA2G4D 25273 510 1127 166 14451 203
## TRIM69 44 23 26 38 41 18
## SLC51B 33 53 23 38 26 77
## SMAD3 1801 2432 581 1654 720 688
## CYP1A1 1 8 1 4 267 168
## AKAP13 3252 4739 1200 3338 1372 1250
## MEFV 359 25 45 16 94 11
## SOCS1 675 174 149 193 300 99
## ATXN2L 2322 2119 648 827 994 613
## CD19 39 10 4 10 7 0
## ITGAL 795 382 209 112 200 50
## ITGAM 526 584 240 517 219 215
## ITGAX 1329 244 133 112 124 65
## DNAJA2 8900 3290 2865 3238 4709 2711
## SIAH1 1227 1011 595 749 587 490
## ADCY7 2179 2256 536 1270 845 488
## NOD2 2495 442 449 294 1529 226
## CMTM2 90 7 4 0 9 0
## SF3B3 6872 3514 1816 2032 3734 1514
## PSMD7 9046 3441 3665 3599 4668 2841
## WWOX 538 121 300 161 270 169
## MAF 5815 6336 2030 4119 2450 2489
## SLC7A5 4562 1310 1780 465 3278 407
## CXCL16 2848 782 691 825 1442 637
## XAF1 3837 1215 1605 2377 3529 306
## CD68 3759 1907 1540 3652 2409 1131
## PER1 1492 3022 1180 2407 1065 2594
## NOS2 1748 11 4 5 1629 7
## TRAF4 1392 960 552 527 851 457
## CCL2 1956 274 425 304 811 373
## CCL5 1146 327 244 130 271 112
## CCL3 970 10 10 9 84 2
## CCR7 682 69 191 41 217 27
## STAT3 24557 6789 5060 4343 19163 3543
## SOST 492 0 1 7 678 8
## ITGA2B 44 22 16 6 8 3
## EFCAB13 57 197 14 35 13 32
## NPEPPS 5395 2918 1451 2163 3866 1326
## TBX21 52 17 13 5 6 6
## COL1A1 58448 145865 25171 33479 60303 24155
## MRPS23 4451 1511 1934 1493 1940 1294
## MIR21 4 2 2 1 2 1
## RPS6KB1 1971 1339 552 820 1239 565
## ACE 519 739 152 297 451 250
## ERN1 1181 793 220 324 546 205
## PRTN3 0 0 1 0 0 0
## PLIN5 699 1500 167 149 619 65
## RETN 126 75 5 7 4 8
## CCL25 0 4 0 0 3 0
## ICAM1 2184 933 427 546 800 292
## TYK2 2027 1835 517 701 962 603
## SMARCA4 3528 2553 1103 1490 1662 972
## ACP5 5056 1516 658 1285 2396 929
## JUNB 17602 2097 2697 1803 8472 2207
## CYP4F22 4878 1271 902 670 3288 562
## JAK3 799 204 155 59 167 47
## JUND 2479 2523 1624 2717 1668 2081
## CEBPA 11992 4793 4817 4847 6013 2917
## CEBPG 4791 1978 1706 1937 2970 1261
## ACP7 11807 263 482 132 6800 101
## ZFP36 5543 1192 640 806 2540 685
## TGFB1 3009 1513 901 1512 1205 1094
## RPS19 102946 43291 36856 40331 35363 40373
## PSG2 5 3 2 1 15 3
## FUT2 2121 172 98 60 973 90
## NKG7 456 92 94 77 139 54
## ZNF415 317 482 47 136 48 128
## LILRB2 724 166 385 191 230 90
## LILRA5 188 5 196 26 58 15
## KIR3DL1 4 2 0 0 1 0
## KIR3DL2 8 0 3 2 0 0
## SMOX 1906 369 401 251 1636 199
## BMP2 1036 314 163 356 807 209
## GINS1 1226 260 561 219 640 264
## PLCG1 2917 2879 849 1194 1479 712
## YWHAB 21487 10270 10163 11928 11157 7069
## PI3 298441 160 10584 101 156252 1254
## MMP9 1416 134 164 244 269 89
## CD40 1242 590 442 605 526 386
## PFDN4 2211 1034 876 1021 675 773
## BMP7 1169 1072 757 896 856 512
## RPS21 73356 34706 25486 22105 19464 24816
## MX1 7385 818 2991 4967 7680 453
## PFKL 5111 4314 1803 2916 2695 2651
## IL17RA 1448 1297 473 783 560 272
## UBE2L3 7273 2796 2840 2811 3532 2245
## TPST2 2032 834 601 730 953 693
## XBP1 16801 6993 3642 4993 7720 4356
## UQCR10 6295 2629 2148 1712 2126 1472
## SEC14L2 1228 394 259 265 330 257
## APOL6 5062 1252 1330 1840 3984 563
## APOL1 6776 463 803 757 2589 196
## TYMP 30119 1838 4413 2309 14052 1182
## BEND2 0 0 0 0 2 1
## PHEX 160 50 18 66 60 24
## TIMP1 2417 2401 2497 3342 2611 1960
## FOXP3 440 117 106 32 95 34
## MSN 14451 7342 6938 8228 8277 7157
## PGK1 48135 13534 16511 11229 18452 10649
## LAMP2 14376 7441 3815 5520 5803 3708
## CD40LG 94 45 40 31 10 7
## IRAK1 5580 3315 2179 3416 3181 2044
## COX2 88595 66196 23164 45380 23436 24216
## GSM5630020 GSM5630021 GSM5630022 GSM5630023 GSM5630024 GSM5630025
## TNFRSF9 96 18 21 4 15 19
## ENO1 37570 12977 9637 11113 14298 15243
## PIK3CD 250 185 81 117 83 210
## PGD 4939 3851 1319 1227 1419 1684
## MTHFR 630 446 157 336 198 342
## TNFRSF1B 709 401 189 317 197 319
## PINK1 1396 1710 479 1487 771 1257
## IFNLR1 201 140 60 86 82 134
## RUNX3 606 426 109 398 226 532
## SH3BGRL3 6500 2717 1772 3456 2796 2767
## CD52 781 420 796 219 579 265
## IFI6 12915 488 7394 1661 932 847
## ZC3H12A 5574 638 698 121 1120 319
## UTP11 1643 752 1059 843 1258 841
## JUN 1439 1018 264 1919 359 872
## KANK4 9 97 9 60 14 9
## EFCAB7 158 151 88 237 123 168
## IL23R 16 3 2 0 2 0
## ADGRL2 1144 788 269 388 302 297
## GBP3 1411 452 623 625 492 482
## GBP1 2775 508 545 733 469 540
## GBP5 383 71 97 70 59 57
## TGFBR3 707 1107 1126 2258 1113 1236
## VCAM1 544 417 339 328 329 203
## PTPN22 114 60 30 34 43 29
## CD160 8 9 13 11 7 10
## FCGR1A 51 10 32 14 33 6
## MCL1 6176 3277 1711 2395 2351 3508
## CTSK 10696 8379 10507 20973 10380 6973
## RORC 123 573 35 271 138 667
## S100A9 774869 19660 707780 1284 383484 3016
## S100A12 3671 22 2017 10 869 0
## S100A8 499912 15523 638183 952 339681 2336
## IL6R 590 343 188 310 213 398
## RIT1 1138 506 598 667 580 637
## BGLAP 21 9 10 16 13 18
## IFI16 4714 1410 1518 1363 1529 1758
## AIM2 189 18 21 13 9 15
## CRP 0 1 0 0 0 0
## FCGR2A 331 133 135 168 113 109
## HSPA6 207 73 110 116 98 84
## FCGR3A 203 39 87 49 102 26
## SELL 238 30 49 40 36 27
## GLUL 34344 8002 8429 6340 9445 8277
## PTGS2 69 16 22 46 21 21
## CRB1 0 0 0 4 0 2
## KDM5B 1712 1250 440 811 600 1312
## IL10 21 13 22 3 14 8
## YOD1 3287 757 1570 769 1122 671
## HHAT 137 134 35 57 95 123
## TRAF5 153 104 94 178 105 107
## NLRP3 54 24 37 40 21 26
## LINC01250 1 0 0 0 0 0
## RPS7 45555 46683 63300 45743 62370 40150
## RSAD2 914 40 317 170 75 53
## FOSL2 3369 2809 609 1200 817 2177
## REL 1179 744 654 370 687 531
## TGFA 3758 812 952 415 942 636
## DYSF 215 184 32 148 63 110
## HK2 2621 920 443 229 412 344
## CD8A 349 76 57 78 117 111
## CD8B 190 45 55 132 68 37
## EIF5B 2826 2156 1318 3015 2103 2859
## IL1R1 1631 1198 585 864 807 1430
## RGPD6 153 164 74 214 137 306
## IL1A 45 50 17 17 15 24
## IL1B 125 59 12 0 28 16
## IL37 358 462 111 695 324 1105
## IL36RN 25527 795 4944 1067 7372 1293
## IL1F10 92 26 53 118 36 133
## IL1RN 4393 1256 910 960 821 1648
## NMI 1571 561 1226 686 1036 511
## TNFAIP6 108 57 539 524 139 15
## IFIH1 1157 376 507 468 250 485
## SCN1A 3 2 0 0 2 4
## ABCB11 28 78 1 5 1 20
## RBM45 189 94 93 114 106 139
## FRZB 441 268 238 666 208 1175
## TFPI 473 380 479 673 309 437
## STAT1 8932 1703 1635 1548 1053 1677
## NABP1 634 216 138 98 142 123
## SF3B1 5241 3702 1727 2905 2741 3670
## CASP10 543 186 171 262 144 191
## CD28 203 49 44 16 82 19
## CTLA4 143 26 38 3 46 10
## ICOS 128 34 55 2 30 9
## NDUFS1 3535 2608 1397 1219 1630 1699
## CXCR2 654 148 83 38 153 110
## IRS1 183 410 98 477 150 241
## CCL20 128 22 56 0 53 26
## ATG16L1 602 529 267 430 347 461
## GPR35 27 13 16 31 8 11
## PDCD1 52 8 17 4 2 16
## PPARG 83 905 121 309 83 119
## RPL15 58598 57112 51282 55971 67837 57432
## EOMES 23 10 8 6 12 16
## CX3CR1 155 108 30 28 41 135
## ACKR2 325 12 73 66 207 12
## CCR2 281 142 138 112 150 216
## CCRL2 37 18 16 12 17 26
## TLR9 12 6 10 7 4 3
## ARHGEF3 1043 545 572 658 641 575
## ADAMTS9 90 82 37 157 57 47
## TMEM45A 30836 16909 12639 5538 18992 13019
## CD80 30 4 10 1 9 3
## CD86 284 165 133 121 135 161
## MIX23 494 320 525 412 351 204
## PLS1 188 64 62 121 104 132
## PTX3 52 17 53 48 55 23
## GOLIM4 696 515 411 1038 423 623
## MYNN 977 689 595 833 739 770
## TNFSF10 5776 2464 3780 2334 3356 2732
## ADIPOQ 14 99 193 1061 60 20
## SPON2 537 341 169 930 297 633
## S100P 2431 461 1801 300 712 223
## WDR1 8847 3428 2121 3808 2669 3941
## CD38 46 4 26 11 2 5
## PPARGC1A 37 204 30 146 92 218
## TMPRSS11B 1 0 0 0 0 2
## CSN3 0 0 0 1 1 0
## ALB 0 0 1 7 0 1
## CXCL8 1305 12 536 30 63 1
## CXCL2 139 5 54 3 16 1
## AREG 371 193 187 40 136 89
## CXCL10 356 63 150 50 48 92
## CXCL13 476 20 42 3 148 13
## SPP1 24 15 2 35 71 48
## HERC6 1689 150 569 131 98 211
## NFKB1 1500 783 345 633 533 1039
## SEC24B 692 598 363 625 362 526
## EGF 12 56 10 35 25 45
## IL2 3 2 8 3 1 2
## IL21 13 1 5 0 4 0
## IL21-AS1 6 0 2 0 0 0
## SLC7A11 287 36 114 26 113 42
## IL15 128 91 135 77 81 72
## EDNRA 280 157 122 302 150 105
## TLR2 265 197 85 96 119 147
## FGB 0 0 0 0 0 0
## DDX60 1486 439 562 341 237 471
## SPCS3 4672 2529 2616 2201 2965 1869
## TLR3 104 90 51 109 57 114
## OSMR 876 359 279 322 288 283
## GZMK 117 48 108 41 46 65
## GZMA 311 86 280 89 136 70
## ANKRD55 9 12 6 1 11 8
## CENPK 282 120 278 105 261 95
## CAST 7701 4570 2394 5144 3409 6877
## ERAP1 1546 1251 649 1280 937 2389
## ERAP2 97 85 106 69 36 40
## TNFAIP8 1689 1040 1117 1175 1364 1435
## CSF2 5 2 10 3 17 0
## IL5 0 1 0 1 0 3
## IL13 2 1 2 2 1 4
## IL4 0 0 0 2 1 0
## CD14 1177 401 794 1241 536 372
## CSNK1A1 14995 5512 6097 5234 7323 6103
## PPARGC1B 347 242 108 84 129 144
## TNIP1 5127 1861 1072 1621 1371 1623
## ATOX1 1526 557 1120 627 940 564
## FAXDC2 952 2159 198 528 286 626
## IL12B 24 1 7 1 9 3
## MIR146A 3 1 2 1 3 1
## PDLIM7 580 269 213 684 432 371
## SERPINB1 4171 802 2343 1474 1692 1052
## SSR1 3817 2277 1578 1835 1663 2317
## CD83 488 201 164 120 154 164
## SOX4 673 651 283 973 444 645
## CMAHP 190 417 169 334 122 420
## HLA-A 30769 14370 9247 13771 14728 28915
## HLA-C 36450 16843 11792 14550 12072 21635
## HLA-B 53675 22243 18825 18866 15806 28691
## MICA 265 160 246 368 302 416
## LTA 23 21 7 5 4 4
## TNF 122 63 36 29 51 43
## HLA-DRB1 8988 4464 5554 4999 2970 3164
## HLA-DQB1 7659 3706 680 842 478 822
## PPARD 3154 848 435 450 500 593
## CCND3 1270 673 448 631 681 1101
## VEGFA 825 665 146 306 303 316
## RUNX2 69 85 18 48 32 34
## IL17A 34 0 8 0 11 0
## IL17F 31 0 69 0 98 0
## PRDM1 2298 556 741 276 667 315
## ATG5 1091 820 1105 968 1030 841
## TRAF3IP2 1053 599 216 571 418 764
## NCOA7 1281 594 494 585 396 430
## SGK1 5068 1634 1503 1217 1841 1921
## IFNGR1 6677 3177 4284 2773 4157 2864
## TNFAIP3 522 370 140 273 159 319
## SOD2 13116 4559 4326 3055 5523 3459
## LPAL2 5 1 4 8 1 0
## PLG 0 0 0 0 0 0
## CCR6 191 106 49 31 61 109
## RAC1 8194 4432 3547 4714 4792 6062
## ZNF316 451 552 118 434 181 426
## AHR 2273 2100 1931 2448 1872 1888
## IL6 18 3 2 0 9 2
## TOMM7 4241 3793 4756 5568 5132 5403
## CYCS 12588 4187 7453 3055 6655 3202
## AQP1 7474 4187 3779 13154 4354 7726
## NT5C3A 6450 1024 3751 664 3245 672
## EGFR 1584 2707 558 1365 910 2696
## CD36 8026 2655 3420 1766 3731 860
## SAMD9 1421 151 370 163 69 148
## SERPINE1 131 54 62 112 105 47
## CUX1 1197 723 204 718 485 1098
## PSMC2 5921 2968 2693 2445 3555 3237
## NAMPT 12323 2828 6207 1543 5609 1977
## HYAL4 638 14 276 1 103 9
## LEP 3 34 71 313 22 11
## CALD1 3326 2352 1478 5325 3935 3952
## BRAF 389 399 120 314 219 340
## EZH2 1088 409 297 252 346 360
## DNAJB6 10896 3641 3555 3033 5035 3670
## CSMD1 17 30 2 30 14 14
## CTSB 18056 7529 4698 6120 7195 7064
## EGR3 1675 1817 326 406 398 627
## TNFRSF10A 272 121 120 70 94 118
## BNIP3L 8212 5933 3637 3647 4247 3492
## DUSP4 420 574 120 224 150 337
## NRG1 86 88 10 59 64 179
## RPL7 67207 71451 50366 73437 73753 84404
## IL7 196 204 172 199 152 210
## GEM 479 139 107 252 198 131
## MYC 2069 1217 364 1241 534 1139
## GPT 224 1620 44 91 135 329
## JAK2 685 481 290 411 359 498
## CD274 281 32 52 14 66 27
## IL33 2104 1678 1973 1581 1378 2311
## LURAP1L-AS1 2 1 1 3 2 3
## LURAP1L 469 310 274 501 403 433
## TTC39B 2639 2361 1346 664 1181 720
## IFNA1 1 0 0 0 0 0
## TEK 135 106 55 120 48 191
## TRBV20OR9-2 0 0 0 0 0 0
## TOMM5 2614 1453 2842 1615 2521 1315
## ANXA1 22024 5453 6871 11874 5580 9477
## ERP44 1695 801 894 886 1053 880
## ZNF483 56 66 21 63 33 57
## TNFSF15 59 45 5 5 26 28
## TLR4 446 234 487 702 255 215
## PTGS1 2583 2493 1255 2307 1800 3871
## HSPA5 12710 6232 4546 3965 5566 4861
## FNBP1 1781 1065 493 939 793 1183
## CARD9 199 113 26 24 38 84
## TRAF2 404 283 85 155 141 372
## CLIC3 3077 919 707 733 1353 1627
## IL2RA 130 52 47 3 29 9
## GATA3 1054 2086 643 2261 893 3518
## VIM 20125 13875 12175 33509 12717 17579
## CREM 537 380 451 359 418 301
## DKK1 10 24 21 24 13 12
## MBL2 0 0 0 0 0 0
## SAR1A 2883 1751 1713 2133 2118 2048
## PRF1 130 49 53 52 55 67
## ZMIZ1 1302 1297 293 907 415 918
## IFIT3 2116 364 1219 915 536 551
## TALDO1 7107 4849 3045 2536 3462 3348
## IGF2 186 112 85 407 59 104
## INS-IGF2 171 109 82 394 59 97
## IGF2-AS 0 0 0 0 0 1
## INS 0 0 0 0 0 0
## STIM1 1405 1001 293 893 526 1439
## TRIM22 2837 621 901 745 462 672
## PTH 0 0 0 0 0 0
## SAA1 548 1277 573 267 130 53
## SLC1A2 41 42 46 131 23 64
## FOSL1 218 30 7 18 11 7
## CCND1 1606 2614 492 2279 1072 5060
## JRKL 96 110 25 79 70 137
## MMP7 227 561 163 592 366 689
## MMP1 110 2 18 0 63 0
## MMP3 1 4 5 7 3 1
## CASP1 2445 1017 1332 880 1406 1247
## DLAT 1281 754 446 538 567 636
## IL18 2847 2809 3149 3178 3432 3767
## CD3E 634 150 78 80 114 120
## MCAM 1324 1355 747 1530 1482 760
## WNK1 2033 2676 547 1801 996 2500
## TNFRSF1A 4220 1937 1081 1885 1713 2989
## GAPDH 55410 16880 22492 26363 24828 27284
## CD4 1263 625 229 561 344 541
## SLC2A3 356 175 78 220 94 112
## CLEC4D 8 3 0 0 7 0
## KLRB1 169 60 50 21 60 36
## CD69 204 130 120 175 106 56
## CLEC2B 2256 2021 4954 3120 4309 3078
## OLR1 18 4 3 3 0 0
## ABCD2 31 18 20 41 11 16
## LRRK2 248 225 134 268 77 168
## VDR 902 1043 143 370 212 769
## TMBIM6 15662 19549 5893 9035 8211 12515
## PFDN5 9477 10802 14793 12825 14027 9877
## SP7 0 2 2 0 0 1
## MUCL1 2756 594 1542 2680 2442 4309
## CD63 14169 10531 15994 26368 15376 13365
## SUOX 1548 1577 331 1113 647 1516
## RPS26 20908 17177 21383 14707 10301 4631
## RPL41 113201 120865 146000 115881 155941 111490
## IL23A 54 5 8 5 8 7
## DDIT3 278 198 266 304 306 305
## IFNG 27 3 12 0 2 4
## IL22 16 1 12 0 19 0
## LYZ 15003 4624 9678 3079 7529 1821
## CAPS2 33 36 25 81 25 54
## SYT1 21 10 5 10 7 14
## BTG1 6322 4714 3876 4873 4524 6509
## HCAR3 790 221 181 121 263 256
## GJB2 44796 2772 9977 959 4406 972
## GJB6 19922 1443 5414 1216 3343 1226
## TNFSF11 9 18 0 1 5 3
## TPT1 212598 187659 246631 190402 281652 150539
## LMO7 2549 634 736 477 655 800
## TNFSF13B 320 215 340 192 295 108
## LAMP1 4218 3602 811 2723 1501 3785
## PSME2 4416 1439 2420 1364 1848 1526
## GZMB 344 13 84 8 35 4
## RPL36AL 6397 4474 4734 4672 5098 4006
## PYGL 1510 1065 863 999 1261 1033
## HIF1A 2440 879 732 978 868 1269
## FOS 867 732 80 1525 222 765
## JDP2 703 503 412 1309 554 814
## NOXRED1 14 14 7 9 14 17
## SERPINA1 833 96 97 91 105 48
## CDC42BPB 1593 1529 304 1196 510 1184
## PLA2G4D 11477 432 2108 117 1424 274
## TRIM69 40 11 37 15 35 18
## SLC51B 28 18 22 34 29 15
## SMAD3 623 663 252 820 262 873
## CYP1A1 4 3 24 31 6 5
## AKAP13 1143 1480 479 1328 652 1132
## MEFV 32 15 11 16 9 11
## SOCS1 185 76 74 48 30 38
## ATXN2L 889 846 150 573 223 604
## CD19 10 6 0 1 3 0
## ITGAL 285 136 51 58 60 40
## ITGAM 262 138 159 304 99 113
## ITGAX 142 138 31 44 52 39
## DNAJA2 4775 2252 2693 2252 2936 2600
## SIAH1 556 485 426 489 426 446
## ADCY7 931 644 244 513 285 425
## NOD2 1313 377 295 205 302 245
## CMTM2 1 1 0 0 4 0
## SF3B3 3504 2263 746 980 894 2252
## PSMD7 5071 2926 2391 2441 3142 2525
## WWOX 383 139 188 150 76 149
## MAF 2399 2334 1196 2737 1685 3000
## SLC7A5 2676 1554 431 172 274 519
## CXCL16 1237 512 236 310 295 528
## XAF1 1330 343 430 282 121 200
## CD68 1496 571 760 1164 644 544
## PER1 1838 1835 500 1143 677 1417
## NOS2 589 1 8 9 0 3
## TRAF4 734 581 319 524 618 601
## CCL2 514 287 291 213 432 256
## CCL5 348 158 144 73 133 111
## CCL3 57 7 13 9 9 3
## CCR7 269 68 80 19 83 43
## STAT3 13640 3866 2535 2205 3452 3193
## SOST 384 3 3 8 5 108
## ITGA2B 12 4 3 3 0 2
## EFCAB13 14 50 35 48 19 21
## NPEPPS 2937 1429 899 1207 1092 1428
## TBX21 20 7 2 1 12 0
## COL1A1 20243 25662 6687 26164 18324 19065
## MRPS23 1823 1027 2009 1366 1769 1059
## MIR21 2 0 0 1 1 0
## RPS6KB1 826 545 259 445 389 518
## ACE 222 227 87 229 95 92
## ERN1 414 278 103 141 147 231
## PRTN3 0 0 3 3 0 0
## PLIN5 607 1583 184 30 79 81
## RETN 2 6 14 13 13 9
## CCL25 0 0 0 0 0 3
## ICAM1 637 194 167 222 206 216
## TYK2 835 734 175 513 221 593
## SMARCA4 1499 1238 326 770 505 1372
## ACP5 2520 1718 869 592 1000 734
## JUNB 9964 3418 1978 1367 2485 2788
## CYP4F22 3885 1368 516 323 684 769
## JAK3 258 118 58 40 38 65
## JUND 1370 1319 433 1653 796 956
## CEBPA 7541 4773 3032 2774 3643 4089
## CEBPG 2285 1252 866 1225 1311 1673
## ACP7 6755 444 940 71 588 84
## ZFP36 1990 832 270 604 423 625
## TGFB1 1160 592 286 985 425 862
## RPS19 48495 37483 44846 40991 52440 47595
## PSG2 15 1 2 0 1 1
## FUT2 792 59 182 24 196 102
## NKG7 174 45 55 26 48 17
## ZNF415 101 194 49 130 100 243
## LILRB2 138 51 99 72 32 22
## LILRA5 22 4 28 20 15 2
## KIR3DL1 2 0 0 0 0 0
## KIR3DL2 0 2 2 0 0 0
## SMOX 1116 263 315 185 472 155
## BMP2 608 171 148 247 101 348
## GINS1 489 203 266 186 284 153
## PLCG1 1631 994 299 672 437 714
## YWHAB 11307 6516 4012 6199 5501 7046
## PI3 99865 240 13235 102 8968 273
## MMP9 689 161 56 33 77 61
## CD40 469 243 235 248 360 455
## PFDN4 810 699 1462 955 1218 628
## BMP7 753 436 223 423 371 602
## RPS21 28960 27690 44577 28206 39415 22980
## MX1 2284 226 460 402 125 278
## PFKL 3138 3461 549 1885 1029 2436
## IL17RA 508 508 168 505 190 303
## UBE2L3 3509 1791 1594 1643 1950 1978
## TPST2 889 543 364 591 416 575
## XBP1 6672 4306 2845 3237 3616 2746
## UQCR10 2744 2134 2210 1376 2155 1411
## SEC14L2 571 299 155 189 194 147
## APOL6 2273 473 556 515 345 466
## APOL1 1201 220 153 198 162 183
## TYMP 17668 1942 1912 692 1239 780
## BEND2 0 0 0 1 1 1
## PHEX 85 27 62 15 34 25
## TIMP1 1076 712 699 1802 808 843
## FOXP3 209 87 24 17 26 47
## MSN 7382 3125 1480 4559 2956 4843
## PGK1 24123 11431 15441 9290 17116 10233
## LAMP2 5693 4604 2747 3397 3843 4406
## CD40LG 62 20 8 3 31 21
## IRAK1 3429 1560 660 1629 1082 1291
## COX2 20359 27200 9003 23684 14876 29334
## GSM5630026 GSM5630027 GSM5630028 GSM5630029 GSM5630030 GSM5630031
## TNFRSF9 33 6 28 16 9 34
## ENO1 10687 14323 17343 7352 5292 10629
## PIK3CD 35 143 149 155 70 79
## PGD 883 2312 2287 1283 1293 1366
## MTHFR 129 327 179 197 159 164
## TNFRSF1B 161 272 506 443 198 227
## PINK1 325 1425 716 924 808 497
## IFNLR1 18 93 113 102 48 68
## RUNX3 94 554 469 347 194 169
## SH3BGRL3 1196 2709 3495 1948 1316 2123
## CD52 132 196 331 209 223 287
## IFI6 1010 810 2350 728 287 6851
## ZC3H12A 567 245 2409 100 90 1990
## UTP11 662 616 606 358 365 705
## JUN 138 627 556 715 332 356
## KANK4 7 38 15 11 17 3
## EFCAB7 50 155 38 47 90 47
## IL23R 4 0 1 0 0 1
## ADGRL2 194 487 311 444 310 325
## GBP3 256 238 335 256 232 554
## GBP1 757 334 635 378 219 1170
## GBP5 86 55 102 69 60 161
## TGFBR3 215 959 492 1364 457 257
## VCAM1 122 258 204 295 132 225
## PTPN22 34 26 46 39 34 52
## CD160 2 15 7 9 7 4
## FCGR1A 44 5 33 4 6 17
## MCL1 1419 2541 2634 2432 1658 2054
## CTSK 7680 13707 6568 8423 9030 4030
## RORC 20 454 65 235 227 45
## S100A9 239207 1230 242363 106 750 217771
## S100A12 1200 0 1175 2 0 1070
## S100A8 236857 1680 177369 165 970 208491
## IL6R 205 297 224 260 179 142
## RIT1 506 523 419 297 278 397
## BGLAP 3 10 6 3 4 6
## IFI16 769 1270 1842 1246 689 2665
## AIM2 23 14 45 16 10 56
## CRP 0 0 0 0 0 1
## FCGR2A 168 86 166 175 117 92
## HSPA6 139 41 119 86 35 122
## FCGR3A 93 29 98 40 29 131
## SELL 47 38 116 47 33 72
## GLUL 5021 7025 8063 4411 3348 7172
## PTGS2 22 21 32 50 4 28
## CRB1 0 2 0 2 1 0
## KDM5B 345 946 633 701 532 703
## IL10 25 14 13 7 16 1
## YOD1 1016 491 1211 685 691 1204
## HHAT 39 129 60 88 31 68
## TRAF5 44 136 59 95 61 61
## NLRP3 3 21 41 53 21 31
## LINC01250 0 0 0 0 0 0
## RPS7 28812 28010 18767 14089 18740 19735
## RSAD2 42 49 85 94 49 544
## FOSL2 422 1682 1777 1879 1052 1138
## REL 254 451 491 403 241 460
## TGFA 541 573 930 287 493 1033
## DYSF 44 140 154 197 52 63
## HK2 353 493 992 364 269 559
## CD8A 96 52 203 229 93 187
## CD8B 56 53 89 107 58 81
## EIF5B 831 2389 1916 1946 1061 1015
## IL1R1 643 1304 792 872 736 646
## RGPD6 68 216 69 136 97 73
## IL1A 43 63 12 20 48 9
## IL1B 23 14 39 6 13 20
## IL37 167 615 145 791 1004 186
## IL36RN 4060 626 6898 998 721 6941
## IL1F10 95 94 18 101 61 38
## IL1RN 1067 1038 1178 674 855 1459
## NMI 681 409 586 331 253 764
## TNFAIP6 235 101 54 130 42 45
## IFIH1 123 255 255 208 122 576
## SCN1A 0 0 1 1 0 1
## ABCB11 0 22 1 2 15 5
## RBM45 51 89 62 79 33 87
## FRZB 179 648 211 309 150 113
## TFPI 201 367 204 377 300 235
## STAT1 1439 1172 2088 1440 915 3942
## NABP1 149 129 145 60 94 114
## SF3B1 1333 3397 1917 2109 1629 1824
## CASP10 108 160 141 143 70 164
## CD28 28 35 59 38 27 44
## CTLA4 16 16 23 4 8 36
## ICOS 12 13 36 6 9 48
## NDUFS1 925 1771 1301 946 891 1298
## CXCR2 99 67 231 37 40 262
## IRS1 93 261 107 270 123 52
## CCL20 33 4 56 0 1 147
## ATG16L1 195 403 218 224 209 149
## GPR35 9 12 12 5 2 6
## PDCD1 7 8 24 12 1 13
## PPARG 44 344 53 94 301 27
## RPL15 24873 40067 24809 22465 25085 22858
## EOMES 6 2 15 35 8 10
## CX3CR1 17 105 80 175 76 40
## ACKR2 49 14 98 22 4 76
## CCR2 54 214 162 217 151 78
## CCRL2 29 21 10 10 20 13
## TLR9 1 6 11 5 1 1
## ARHGEF3 308 549 555 510 248 496
## ADAMTS9 41 82 45 82 40 16
## TMEM45A 9435 10999 10985 7152 8571 13480
## CD80 13 6 9 2 0 8
## CD86 92 139 114 139 105 123
## MIX23 295 184 186 93 132 210
## PLS1 38 81 64 62 34 74
## PTX3 33 33 29 19 14 6
## GOLIM4 204 734 412 750 325 213
## MYNN 441 535 301 281 288 334
## TNFSF10 1827 1470 1926 857 831 2423
## ADIPOQ 124 237 112 173 61 0
## SPON2 153 502 359 474 185 134
## S100P 1353 611 1120 141 60 491
## WDR1 2169 3767 4923 2864 1609 2618
## CD38 5 3 30 7 1 17
## PPARGC1A 26 454 34 77 121 8
## TMPRSS11B 0 0 0 0 0 0
## CSN3 0 0 0 0 0 0
## ALB 1 0 0 0 0 0
## CXCL8 386 0 215 3 3 270
## CXCL2 39 0 19 2 1 52
## AREG 130 77 86 10 22 125
## CXCL10 168 8 123 44 25 152
## CXCL13 127 6 44 1 2 317
## SPP1 10 4 2 13 4 52
## HERC6 76 151 230 124 66 958
## NFKB1 268 661 720 614 347 525
## SEC24B 163 455 314 379 236 266
## EGF 2 30 6 27 10 2
## IL2 0 0 1 0 3 1
## IL21 0 1 3 0 0 6
## IL21-AS1 0 0 2 0 0 4
## SLC7A11 119 16 93 29 9 134
## IL15 35 69 60 41 50 47
## EDNRA 103 256 152 258 105 127
## TLR2 61 178 94 82 108 115
## FGB 0 0 0 0 0 0
## DDX60 122 311 249 396 229 824
## SPCS3 2127 1600 1719 1303 1078 1644
## TLR3 31 54 62 99 57 66
## OSMR 167 391 325 227 173 308
## GZMK 41 22 79 91 23 82
## GZMA 111 28 119 129 37 140
## ANKRD55 0 3 7 2 2 3
## CENPK 104 66 82 44 50 107
## CAST 2264 6039 3617 4492 2696 3179
## ERAP1 414 1439 773 1114 602 637
## ERAP2 179 434 779 890 135 765
## TNFAIP8 671 664 683 461 599 805
## CSF2 0 1 2 1 3 5
## IL5 0 1 0 0 0 0
## IL13 0 4 1 1 0 0
## IL4 0 1 0 2 0 0
## CD14 440 444 784 889 337 309
## CSNK1A1 5291 4449 6274 3873 2498 6057
## PPARGC1B 31 174 126 74 40 118
## TNIP1 841 1651 2371 1287 885 1674
## ATOX1 507 512 607 323 244 703
## FAXDC2 142 1420 388 611 835 265
## IL12B 12 1 3 0 1 7
## MIR146A 2 0 2 0 0 1
## PDLIM7 154 451 418 269 98 180
## SERPINB1 1891 1003 1673 717 406 1226
## SSR1 1314 1964 1678 1567 1038 1390
## CD83 142 109 182 98 59 154
## SOX4 173 670 420 509 346 213
## CMAHP 144 325 40 140 247 37
## HLA-A 7093 11439 18276 14444 6461 11896
## HLA-C 4968 8412 21325 15804 8280 16970
## HLA-B 7940 14594 25613 16977 11789 19611
## MICA 179 287 112 109 130 122
## LTA 5 6 14 16 2 8
## TNF 19 18 43 40 14 37
## HLA-DRB1 3229 5597 4924 4670 5064 4975
## HLA-DQB1 544 1410 781 729 592 751
## PPARD 399 636 1312 450 374 827
## CCND3 246 765 667 553 299 436
## VEGFA 93 582 469 266 138 204
## RUNX2 18 60 35 48 16 19
## IL17A 8 0 8 0 0 15
## IL17F 3 0 11 0 0 8
## PRDM1 340 282 822 287 285 804
## ATG5 572 701 414 338 349 481
## TRAF3IP2 308 610 529 486 275 402
## NCOA7 437 444 484 334 226 511
## SGK1 1446 1545 1765 616 502 2021
## IFNGR1 2970 1875 2102 1353 1331 2402
## TNFAIP3 120 234 210 283 136 152
## SOD2 6032 3715 4397 2013 1900 4636
## LPAL2 3 3 2 1 3 1
## PLG 0 7 0 0 1 0
## CCR6 23 104 102 121 50 48
## RAC1 2200 4598 4904 3838 2694 3584
## ZNF316 67 397 258 340 190 118
## AHR 769 1738 1083 1300 688 1135
## IL6 6 2 3 0 0 8
## TOMM7 1854 3257 1837 2022 2629 1972
## CYCS 6908 2977 4287 1489 1433 4031
## AQP1 2045 6446 5403 9081 2531 2315
## NT5C3A 2040 587 2045 482 588 2533
## EGFR 246 2262 882 1740 1077 619
## CD36 3164 1921 3785 1342 958 4953
## SAMD9 122 103 270 125 77 646
## SERPINE1 132 43 42 82 20 35
## CUX1 243 714 632 774 292 342
## PSMC2 2149 2418 2097 1459 1433 1979
## NAMPT 9003 1798 5075 1049 1077 5074
## HYAL4 165 28 144 14 10 476
## LEP 7 59 84 87 1 0
## CALD1 1505 5162 2711 3254 1853 1350
## BRAF 129 337 182 220 205 122
## EZH2 318 265 292 130 162 337
## DNAJB6 4447 2701 4122 1910 1887 3504
## CSMD1 0 31 3 4 7 7
## CTSB 6404 9202 8807 6061 4297 6546
## EGR3 156 616 410 407 420 418
## TNFRSF10A 119 117 91 54 49 94
## BNIP3L 2215 4351 3399 2273 2908 2748
## DUSP4 150 380 156 198 173 119
## NRG1 43 77 29 83 26 38
## RPL7 32952 64734 32426 35977 39900 28911
## IL7 64 107 64 90 126 84
## GEM 116 133 171 256 79 120
## MYC 332 506 815 424 253 626
## GPT 22 629 72 129 282 60
## JAK2 116 511 232 360 271 392
## CD274 102 23 86 7 12 85
## IL33 1211 1241 921 501 803 1025
## LURAP1L-AS1 4 1 1 0 4 0
## LURAP1L 100 299 228 256 149 121
## TTC39B 691 1222 727 380 1012 1087
## IFNA1 0 0 0 0 0 0
## TEK 30 122 130 140 44 52
## TRBV20OR9-2 0 0 0 0 1 0
## TOMM5 1509 950 1062 509 707 927
## ANXA1 8105 7381 4489 5273 3484 2876
## ERP44 571 723 736 616 502 565
## ZNF483 8 70 16 29 28 16
## TNFSF15 13 24 9 5 9 25
## TLR4 453 361 197 435 325 136
## PTGS1 528 1922 1354 2135 1574 1444
## HSPA5 12090 4535 5619 3772 2179 4444
## FNBP1 316 876 762 909 527 549
## CARD9 15 64 82 63 31 25
## TRAF2 69 233 227 143 115 108
## CLIC3 619 835 1410 619 340 875
## IL2RA 17 19 27 25 13 25
## GATA3 137 1596 550 1313 1071 308
## VIM 8677 22567 13689 22929 13179 7536
## CREM 399 324 196 154 141 197
## DKK1 34 36 7 6 22 6
## MBL2 0 0 0 0 0 0
## SAR1A 1297 1747 1074 1144 869 1035
## PRF1 22 23 60 61 19 37
## ZMIZ1 161 901 754 984 342 288
## IFIT3 313 329 572 425 261 1299
## TALDO1 1774 3186 2970 1736 2012 2453
## IGF2 49 101 97 133 77 51
## INS-IGF2 48 96 82 120 66 46
## IGF2-AS 1 0 0 0 0 0
## INS 0 0 0 0 0 0
## STIM1 277 1173 832 974 426 478
## TRIM22 515 527 463 516 440 1349
## PTH 0 0 0 0 0 0
## SAA1 182 178 112 17 218 147
## SLC1A2 3 45 5 26 9 41
## FOSL1 66 12 67 6 5 33
## CCND1 340 2798 1232 2871 1137 628
## JRKL 12 103 45 66 55 30
## MMP7 223 1848 199 191 97 195
## MMP1 37 4 9 8 0 45
## MMP3 12 6 3 3 0 6
## CASP1 776 752 978 645 474 1107
## DLAT 235 603 519 407 292 490
## IL18 1776 2820 1056 1584 2469 1173
## CD3E 74 73 250 185 95 193
## MCAM 725 1405 1069 770 530 501
## WNK1 330 2497 1095 2353 1323 712
## TNFRSF1A 1258 1867 1937 1470 901 1410
## GAPDH 16855 24938 26706 13429 9020 19246
## CD4 154 472 616 944 419 350
## SLC2A3 83 136 150 145 55 131
## CLEC4D 6 1 3 0 1 5
## KLRB1 96 42 63 19 23 46
## CD69 49 40 57 42 32 57
## CLEC2B 1223 988 1116 927 1088 1732
## OLR1 8 0 2 0 4 7
## ABCD2 6 15 13 13 21 13
## LRRK2 38 163 89 163 122 91
## VDR 132 664 403 435 280 225
## TMBIM6 5294 13586 7518 6873 8750 5797
## PFDN5 5694 7042 3872 3612 4618 3893
## SP7 0 3 0 0 1 0
## MUCL1 1073 3956 1947 1412 143 439
## CD63 9948 13065 7745 8356 7554 4894
## SUOX 340 1371 740 1048 771 551
## RPS26 10051 9879 8167 5030 2305 4090
## RPL41 61177 78308 44035 38123 48044 51266
## IL23A 4 7 13 3 0 13
## DDIT3 287 281 104 135 147 95
## IFNG 11 0 3 5 1 10
## IL22 2 0 6 0 0 3
## LYZ 3918 2357 6648 4465 2724 3984
## CAPS2 26 53 15 12 20 11
## SYT1 7 9 1 12 16 2
## BTG1 1958 3775 4202 3160 3341 3508
## HCAR3 143 59 216 109 111 241
## GJB2 9677 1948 19498 1039 524 13975
## GJB6 5792 1328 9688 1150 490 5550
## TNFSF11 1 17 8 9 9 6
## TPT1 133918 121748 91598 69548 83508 94538
## LMO7 282 626 794 438 252 937
## TNFSF13B 137 141 162 131 147 128
## LAMP1 692 2987 2622 3386 1776 1605
## PSME2 1382 1005 1621 729 637 1552
## GZMB 84 5 151 35 9 78
## RPL36AL 2565 3421 3161 2583 2331 3030
## PYGL 516 684 1314 521 413 867
## HIF1A 833 1154 1179 817 339 906
## FOS 35 227 111 422 210 925
## JDP2 209 678 399 539 310 303
## NOXRED1 1 4 3 7 3 7
## SERPINA1 148 45 211 115 35 154
## CDC42BPB 239 1124 812 1032 488 450
## PLA2G4D 572 139 4116 187 100 3091
## TRIM69 8 6 16 3 5 11
## SLC51B 16 46 8 12 9 6
## SMAD3 199 622 348 495 407 243
## CYP1A1 1 6 0 65 10 0
## AKAP13 249 1159 587 1278 571 353
## MEFV 4 4 12 9 4 3
## SOCS1 35 45 114 40 47 69
## ATXN2L 132 522 367 346 253 229
## CD19 2 0 0 0 0 0
## ITGAL 12 50 100 134 36 72
## ITGAM 61 161 138 241 93 86
## ITGAX 10 70 53 66 31 43
## DNAJA2 1959 1842 1681 1272 1091 1774
## SIAH1 268 365 309 245 187 236
## ADCY7 116 427 348 472 243 170
## NOD2 217 197 444 168 96 308
## CMTM2 0 1 1 1 0 1
## SF3B3 624 1539 1335 1139 775 1018
## PSMD7 1878 2141 2100 1433 1344 1866
## WWOX 148 205 307 90 74 66
## MAF 468 1730 1505 1758 1003 690
## SLC7A5 348 489 1774 330 195 768
## CXCL16 191 364 702 427 182 401
## XAF1 114 166 293 153 104 710
## CD68 492 736 681 947 571 546
## PER1 288 1024 830 1237 524 344
## NOS2 21 5 184 3 1 104
## TRAF4 302 537 356 151 157 210
## CCL2 212 164 226 102 56 235
## CCL5 59 36 261 255 90 221
## CCL3 11 2 10 5 9 21
## CCR7 15 33 108 37 15 94
## STAT3 1773 3286 4547 2569 1089 2788
## SOST 4 1 28 0 0 30
## ITGA2B 5 8 2 3 3 0
## EFCAB13 16 49 5 31 22 9
## NPEPPS 577 1015 1049 765 679 742
## TBX21 2 5 7 8 11 3
## COL1A1 5839 23680 14109 26893 6080 3426
## MRPS23 863 821 670 411 439 743
## MIR21 1 0 0 0 0 0
## RPS6KB1 230 487 323 403 219 346
## ACE 33 195 144 271 44 60
## ERN1 82 222 222 150 111 126
## PRTN3 0 0 0 0 0 0
## PLIN5 72 470 176 30 237 168
## RETN 1 5 1 2 0 0
## CCL25 0 0 0 2 0 0
## ICAM1 193 216 241 263 135 155
## TYK2 112 461 294 314 219 186
## SMARCA4 146 956 958 788 453 516
## ACP5 543 796 1097 483 615 934
## JUNB 728 1260 3615 1157 616 3150
## CYP4F22 533 881 1414 492 761 997
## JAK3 30 45 93 38 19 54
## JUND 682 1150 1319 1544 504 574
## CEBPA 1261 2633 4526 2275 1854 3347
## CEBPG 802 1128 1052 871 646 1043
## ACP7 390 168 1772 108 141 1552
## ZFP36 151 402 637 421 247 664
## TGFB1 283 603 950 883 283 334
## RPS19 19385 27659 22703 15531 16349 18502
## PSG2 0 0 4 1 0 0
## FUT2 151 81 156 17 6 90
## NKG7 56 9 75 56 13 50
## ZNF415 43 204 30 36 61 18
## LILRB2 30 50 61 84 24 55
## LILRA5 20 2 8 2 0 3
## KIR3DL1 0 0 0 2 0 0
## KIR3DL2 0 0 1 0 0 1
## SMOX 298 215 796 158 200 393
## BMP2 89 133 160 262 83 76
## GINS1 96 101 136 66 71 185
## PLCG1 257 726 585 515 296 439
## YWHAB 2715 6265 5250 4921 3487 4007
## PI3 21003 128 22066 19 33 46097
## MMP9 20 55 209 104 59 172
## CD40 111 204 216 206 138 220
## PFDN4 392 407 297 233 387 434
## BMP7 101 443 645 579 234 255
## RPS21 15655 15485 10991 7946 11660 13162
## MX1 118 264 733 452 111 1715
## PFKL 492 2361 2073 1852 976 774
## IL17RA 87 413 239 283 192 193
## UBE2L3 1124 1685 1730 1079 954 1301
## TPST2 298 317 395 300 144 273
## XBP1 3759 3903 2838 1563 1463 2016
## UQCR10 867 1262 1054 568 649 885
## SEC14L2 101 261 210 96 90 200
## APOL6 317 337 495 395 213 884
## APOL1 231 159 297 200 97 495
## TYMP 1875 1060 4880 616 395 3238
## BEND2 0 0 0 0 0 0
## PHEX 10 26 22 35 20 41
## TIMP1 549 1171 863 1050 654 294
## FOXP3 15 49 41 22 31 60
## MSN 1979 3899 4386 4607 1732 2392
## PGK1 9838 7944 10089 4380 4144 7456
## LAMP2 2040 3983 2279 2303 2252 2337
## CD40LG 5 13 24 21 17 26
## IRAK1 529 1430 1918 1176 651 994
## COX2 4773 23140 10454 13710 12162 7377
## GSM5630032 GSM5630033 GSM5630034 GSM5630035 GSM5630036
## TNFRSF9 13 4 36 37 6
## ENO1 13706 7619 19502 19107 2925
## PIK3CD 172 105 209 465 68
## PGD 3314 1118 2324 5151 502
## MTHFR 370 183 380 930 119
## TNFRSF1B 371 207 1013 991 93
## PINK1 2109 1170 1547 3526 509
## IFNLR1 154 55 109 368 26
## RUNX3 482 344 461 1441 96
## SH3BGRL3 3025 1908 4416 3649 1034
## CD52 256 111 383 1160 444
## IFI6 630 965 1848 1002 483
## ZC3H12A 728 108 1203 914 60
## UTP11 676 424 796 1227 475
## JUN 776 487 1128 3238 212
## KANK4 41 10 18 136 1
## EFCAB7 107 53 102 315 105
## IL23R 0 0 4 3 3
## ADGRL2 568 216 522 1408 130
## GBP3 414 269 402 478 314
## GBP1 325 222 1061 679 150
## GBP5 60 43 195 167 47
## TGFBR3 1201 682 1131 3095 388
## VCAM1 330 120 527 681 164
## PTPN22 28 31 77 123 24
## CD160 8 3 23 20 3
## FCGR1A 6 3 213 16 16
## MCL1 3067 2123 4141 6855 1073
## CTSK 12583 9343 13370 20138 9129
## RORC 713 326 182 1461 119
## S100A9 2797 245 183809 8612 103
## S100A12 6 0 1028 5 0
## S100A8 2567 200 138251 9031 103
## IL6R 366 294 459 817 119
## RIT1 469 272 620 961 261
## BGLAP 17 11 13 17 8
## IFI16 1762 817 2538 3060 482
## AIM2 10 4 49 41 11
## CRP 0 0 0 0 0
## FCGR2A 107 91 443 236 52
## HSPA6 82 59 237 161 21
## FCGR3A 33 39 603 77 26
## SELL 26 25 161 81 14
## GLUL 8541 5475 12587 12226 3051
## PTGS2 16 23 110 107 20
## CRB1 8 1 5 3 5
## KDM5B 1410 519 941 2980 306
## IL10 5 10 22 38 11
## YOD1 612 1000 1746 1440 379
## HHAT 141 45 90 291 31
## TRAF5 87 80 194 291 59
## NLRP3 16 17 79 66 9
## LINC01250 0 0 0 0 0
## RPS7 36522 19857 21978 140215 44088
## RSAD2 52 53 155 101 47
## FOSL2 2654 1615 2601 7737 393
## REL 487 203 709 1550 165
## TGFA 830 440 1075 1378 297
## DYSF 192 105 394 405 37
## HK2 783 192 1240 2067 76
## CD8A 153 22 189 105 83
## CD8B 87 21 57 54 122
## EIF5B 2584 1664 2962 5342 796
## IL1R1 1319 795 2201 3376 535
## RGPD6 239 130 177 506 79
## IL1A 70 32 21 175 42
## IL1B 30 0 144 63 5
## IL37 1147 855 458 1211 596
## IL36RN 1498 1267 6207 2014 675
## IL1F10 83 94 80 126 61
## IL1RN 1824 1004 1781 1945 465
## NMI 580 306 815 770 403
## TNFAIP6 76 54 105 47 72
## IFIH1 351 193 353 742 102
## SCN1A 3 3 1 17 0
## ABCB11 36 6 5 104 8
## RBM45 95 64 117 170 32
## FRZB 150 250 675 1529 141
## TFPI 511 289 525 603 445
## STAT1 1649 1151 3515 3069 477
## NABP1 148 45 307 324 36
## SF3B1 3201 1794 3343 7068 1152
## CASP10 143 84 242 364 79
## CD28 52 12 135 127 33
## CTLA4 5 1 37 41 3
## ICOS 17 4 45 43 9
## NDUFS1 2486 930 1843 4449 615
## CXCR2 122 27 258 171 13
## IRS1 376 244 285 1264 80
## CCL20 13 0 34 33 7
## ATG16L1 375 262 490 1248 130
## GPR35 17 8 23 30 1
## PDCD1 12 7 25 18 4
## PPARG 619 91 245 1062 113
## RPL15 54743 31341 33670 91904 43168
## EOMES 12 3 31 19 14
## CX3CR1 96 57 206 247 32
## ACKR2 7 12 71 62 6
## CCR2 151 110 271 316 135
## CCRL2 10 8 45 16 6
## TLR9 3 4 10 14 0
## ARHGEF3 542 301 794 1006 225
## ADAMTS9 58 54 224 356 17
## TMEM45A 15806 5708 12071 21020 5402
## CD80 1 1 21 14 0
## CD86 127 66 235 288 150
## MIX23 249 144 188 474 202
## PLS1 73 58 86 244 23
## PTX3 29 11 215 65 17
## GOLIM4 633 446 1018 2279 246
## MYNN 546 339 516 1340 489
## TNFSF10 2090 737 2098 3900 1429
## ADIPOQ 263 21 1002 74 9
## SPON2 624 623 626 883 103
## S100P 318 77 683 1243 98
## WDR1 4182 2629 6203 7072 819
## CD38 5 3 21 10 4
## PPARGC1A 173 92 142 869 69
## TMPRSS11B 0 0 0 0 0
## CSN3 0 0 0 2 0
## ALB 6 0 4 5 0
## CXCL8 2 0 3140 20 1
## CXCL2 5 1 31 12 4
## AREG 68 15 77 195 17
## CXCL10 14 1 149 89 17
## CXCL13 7 1 137 31 4
## SPP1 6 7 22 398 4
## HERC6 121 87 267 314 40
## NFKB1 848 498 1025 2355 206
## SEC24B 648 315 507 1545 182
## EGF 23 25 19 175 4
## IL2 4 0 0 16 5
## IL21 0 0 2 0 0
## IL21-AS1 0 0 1 0 0
## SLC7A11 18 9 105 57 10
## IL15 61 47 95 163 77
## EDNRA 183 140 375 558 61
## TLR2 227 48 197 391 53
## FGB 0 0 0 0 0
## DDX60 443 426 446 1026 139
## SPCS3 1802 1245 2607 3711 1234
## TLR3 115 42 114 220 51
## OSMR 348 157 706 972 70
## GZMK 59 19 76 72 99
## GZMA 59 21 123 110 166
## ANKRD55 7 2 4 8 2
## CENPK 81 59 71 170 69
## CAST 5627 3857 5856 10306 1745
## ERAP1 1442 969 1395 3722 437
## ERAP2 1165 291 725 1351 213
## TNFAIP8 903 535 1007 1386 866
## CSF2 0 0 0 13 2
## IL5 2 0 0 0 0
## IL13 0 0 2 5 0
## IL4 1 1 1 1 1
## CD14 608 824 2236 910 267
## CSNK1A1 5554 3661 7705 9079 1639
## PPARGC1B 143 48 159 586 31
## TNIP1 2284 1093 2892 3567 453
## ATOX1 672 405 761 1021 403
## FAXDC2 1736 376 503 5274 175
## IL12B 0 0 4 8 0
## MIR146A 1 0 2 1 0
## PDLIM7 371 237 819 773 62
## SERPINB1 717 614 1351 1271 655
## SSR1 2220 1215 2499 4125 716
## CD83 83 44 238 273 70
## SOX4 600 341 655 2115 203
## CMAHP 188 189 226 569 194
## HLA-A 22716 12097 20862 26458 5940
## HLA-C 28740 9011 21219 22880 6588
## HLA-B 32612 15618 31069 32799 9737
## MICA 208 165 273 725 159
## LTA 11 1 12 34 5
## TNF 24 8 49 149 15
## HLA-DRB1 7685 3549 5694 4544 2750
## HLA-DQB1 1090 1041 903 872 308
## PPARD 792 518 1685 1940 125
## CCND3 1027 650 1268 1703 301
## VEGFA 602 235 505 1279 142
## RUNX2 26 30 74 202 13
## IL17A 0 0 5 0 0
## IL17F 0 0 6 0 0
## PRDM1 349 268 1044 1105 132
## ATG5 680 390 703 1357 559
## TRAF3IP2 799 486 722 1372 137
## NCOA7 530 234 616 1057 197
## SGK1 970 723 1992 2440 344
## IFNGR1 1971 1220 2589 4390 1727
## TNFAIP3 275 128 369 1100 114
## SOD2 4369 2168 13578 8915 1594
## LPAL2 3 0 2 5 0
## PLG 0 3 1 2 1
## CCR6 64 53 103 293 59
## RAC1 5715 3813 5746 7562 1855
## ZNF316 522 343 461 1442 148
## AHR 1605 855 2262 4237 551
## IL6 0 1 3 2 1
## TOMM7 4636 3366 2950 7048 3704
## CYCS 3385 1652 4187 6232 1832
## AQP1 6444 4819 8033 8595 2061
## NT5C3A 853 598 2250 1449 592
## EGFR 2556 1329 1595 8413 538
## CD36 2051 970 4152 5255 385
## SAMD9 109 59 330 427 38
## SERPINE1 45 52 255 73 27
## CUX1 847 592 1220 2065 173
## PSMC2 2681 1806 2960 3744 1269
## NAMPT 2419 1392 4090 3831 1023
## HYAL4 12 4 85 29 1
## LEP 52 11 285 24 3
## CALD1 4120 3015 9375 7847 1166
## BRAF 365 216 365 1023 127
## EZH2 234 165 280 499 106
## DNAJB6 3884 2203 5031 5652 1443
## CSMD1 37 9 19 101 0
## CTSB 8572 5160 17808 18238 1923
## EGR3 1049 542 717 2508 159
## TNFRSF10A 104 49 103 240 20
## BNIP3L 5004 2173 4022 6953 2022
## DUSP4 535 180 350 1810 73
## NRG1 75 48 101 188 34
## RPL7 78092 42079 46472 95916 56925
## IL7 169 126 133 271 153
## GEM 145 98 509 334 73
## MYC 775 549 964 2164 169
## GPT 1656 274 150 2022 112
## JAK2 488 270 604 1133 200
## CD274 15 12 77 82 16
## IL33 1937 806 1047 1903 1646
## LURAP1L-AS1 7 1 1 7 2
## LURAP1L 226 250 473 682 144
## TTC39B 1973 469 917 3245 550
## IFNA1 0 0 0 0 0
## TEK 166 116 272 270 40
## TRBV20OR9-2 0 0 0 0 0
## TOMM5 1236 758 1352 3434 1209
## ANXA1 6776 4775 12695 9859 5955
## ERP44 947 538 1054 1539 408
## ZNF483 67 30 54 154 27
## TNFSF15 26 4 22 83 4
## TLR4 232 331 648 524 354
## PTGS1 2756 2281 1995 5260 870
## HSPA5 6225 3267 8034 13999 1457
## FNBP1 987 738 1288 2358 373
## CARD9 91 35 111 253 10
## TRAF2 283 132 263 640 44
## CLIC3 1448 1405 1467 1527 284
## IL2RA 15 10 68 54 7
## GATA3 2697 1706 822 5751 661
## VIM 25899 20078 34119 33058 9436
## CREM 328 199 374 680 231
## DKK1 21 52 37 32 7
## MBL2 0 0 1 1 0
## SAR1A 1777 1011 1969 3490 902
## PRF1 44 16 107 61 18
## ZMIZ1 965 571 1203 4053 161
## IFIT3 361 329 690 607 299
## TALDO1 4807 2189 3156 5591 1427
## IGF2 269 133 595 323 98
## INS-IGF2 262 121 563 299 95
## IGF2-AS 1 0 0 0 0
## INS 0 0 0 0 0
## STIM1 1305 720 1224 3190 234
## TRIM22 692 666 1089 1539 432
## PTH 0 0 0 0 0
## SAA1 619 33 766 1433 131
## SLC1A2 100 15 32 111 14
## FOSL1 2 19 84 14 0
## CCND1 4079 2294 2376 10044 749
## JRKL 153 73 110 472 25
## MMP7 466 220 320 1287 89
## MMP1 12 0 37 135 3
## MMP3 2 18 13 15 0
## CASP1 862 591 1089 1724 596
## DLAT 784 426 751 1480 171
## IL18 2691 2690 1701 4209 2863
## CD3E 199 48 254 323 92
## MCAM 1620 561 5087 2393 336
## WNK1 2856 1868 2929 9442 729
## TNFRSF1A 3205 1837 2690 3939 569
## GAPDH 22467 16159 29623 29843 6821
## CD4 672 532 1059 1590 299
## SLC2A3 185 103 357 361 68
## CLEC4D 3 0 17 4 0
## KLRB1 45 26 90 96 39
## CD69 55 17 52 215 136
## CLEC2B 1844 1269 1750 4359 2813
## OLR1 1 0 6 11 0
## ABCD2 12 7 42 55 13
## LRRK2 176 175 192 449 105
## VDR 779 300 597 3121 107
## TMBIM6 18799 7515 10465 24582 4585
## PFDN5 8505 4610 6012 39189 10917
## SP7 1 0 1 2 0
## MUCL1 164 387 2983 5242 36
## CD63 12408 10463 15232 21435 11957
## SUOX 1936 920 1315 3318 360
## RPS26 5267 12099 4636 12763 3339
## RPL41 106256 57211 61002 315059 111396
## IL23A 1 2 9 19 1
## DDIT3 303 127 220 625 162
## IFNG 0 1 3 2 0
## IL22 0 0 0 0 0
## LYZ 2200 1196 9034 6003 3225
## CAPS2 53 33 42 125 27
## SYT1 8 11 25 61 15
## BTG1 6365 2980 5203 9067 2908
## HCAR3 182 111 421 402 82
## GJB2 1654 610 8537 4836 173
## GJB6 976 547 5002 3034 378
## TNFSF11 5 1 8 29 7
## TPT1 146293 83460 123801 417950 177950
## LMO7 576 343 1134 1653 161
## TNFSF13B 160 112 371 221 166
## LAMP1 5175 2598 3438 8993 612
## PSME2 1241 724 2398 2177 787
## GZMB 0 8 138 24 14
## RPL36AL 5455 2497 4109 7793 3235
## PYGL 1096 480 1697 1583 220
## HIF1A 811 500 1591 2826 222
## FOS 329 178 355 13831 1032
## JDP2 811 676 698 1090 327
## NOXRED1 10 4 13 22 0
## SERPINA1 47 20 534 238 35
## CDC42BPB 1240 908 1600 3950 259
## PLA2G4D 269 79 2270 580 50
## TRIM69 9 6 16 25 7
## SLC51B 27 8 63 27 9
## SMAD3 815 429 690 1620 251
## CYP1A1 22 184 1 12 3
## AKAP13 1299 837 1679 4769 428
## MEFV 6 4 38 15 5
## SOCS1 70 77 79 136 35
## ATXN2L 737 367 618 2038 149
## CD19 0 0 5 5 0
## ITGAL 75 20 137 272 39
## ITGAM 150 175 219 423 76
## ITGAX 42 23 146 218 24
## DNAJA2 2084 1317 2219 3254 1016
## SIAH1 414 259 451 738 241
## ADCY7 551 409 703 1975 173
## NOD2 222 102 431 674 63
## CMTM2 4 0 14 8 2
## SF3B3 2054 1048 1656 4358 328
## PSMD7 2523 1523 2602 3612 1128
## WWOX 96 67 331 288 46
## MAF 2556 1598 2277 6300 645
## SLC7A5 893 626 965 2056 67
## CXCL16 582 269 777 1142 113
## XAF1 263 156 356 512 115
## CD68 797 895 1887 1436 337
## PER1 1225 806 1191 2108 394
## NOS2 0 8 21 4 3
## TRAF4 538 255 486 1053 199
## CCL2 254 64 236 491 214
## CCL5 229 29 270 173 107
## CCL3 3 4 66 21 2
## CCR7 22 14 160 113 23
## STAT3 3321 1614 5818 9402 608
## SOST 0 9 48 7 0
## ITGA2B 28 7 6 14 6
## EFCAB13 34 21 28 88 11
## NPEPPS 1265 737 1179 2811 309
## TBX21 18 2 16 16 0
## COL1A1 31968 9072 54374 129728 11321
## MRPS23 833 496 910 2156 683
## MIR21 0 1 5 1 0
## RPS6KB1 533 299 698 1283 177
## ACE 185 99 300 469 34
## ERN1 286 115 316 984 92
## PRTN3 0 0 3 0 0
## PLIN5 1060 65 126 1637 72
## RETN 2 0 26 6 1
## CCL25 0 1 0 0 0
## ICAM1 208 138 427 616 99
## TYK2 636 349 646 1578 178
## SMARCA4 1243 670 1219 3952 216
## ACP5 1315 589 1411 1624 326
## JUNB 2528 1413 2912 5495 483
## CYP4F22 1675 617 1682 3040 152
## JAK3 40 23 154 158 24
## JUND 1678 1637 1498 2258 391
## CEBPA 5595 3001 3963 6858 933
## CEBPG 1333 868 1355 2259 581
## ACP7 145 160 1630 703 46
## ZFP36 586 347 975 1961 217
## TGFB1 962 783 1531 1606 232
## RPS19 38949 26146 26689 69794 30625
## PSG2 2 0 0 1 1
## FUT2 40 38 133 220 14
## NKG7 57 25 143 27 26
## ZNF415 183 44 63 265 64
## LILRB2 44 35 287 116 27
## LILRA5 1 4 78 10 6
## KIR3DL1 0 0 0 1 0
## KIR3DL2 0 0 0 5 2
## SMOX 223 263 570 488 35
## BMP2 239 186 247 457 111
## GINS1 150 109 159 222 104
## PLCG1 730 438 918 2468 185
## YWHAB 6650 4301 7098 9943 2251
## PI3 332 53 16297 249 12
## MMP9 92 52 549 797 32
## CD40 395 235 338 499 166
## PFDN4 536 365 427 1265 703
## BMP7 681 460 749 1301 98
## RPS21 23107 12946 13244 102479 27670
## MX1 216 233 571 543 95
## PFKL 3080 1697 2664 6563 490
## IL17RA 515 258 627 1468 91
## UBE2L3 1999 1200 2098 3027 732
## TPST2 522 353 573 867 139
## XBP1 4194 1879 4334 7318 1276
## UQCR10 1822 746 1196 3013 921
## SEC14L2 187 66 306 558 48
## APOL6 402 205 991 1052 181
## APOL1 177 115 638 544 66
## TYMP 1373 705 5913 2483 251
## BEND2 0 0 0 3 2
## PHEX 18 19 31 49 15
## TIMP1 1298 1076 2099 1784 578
## FOXP3 58 33 98 158 7
## MSN 4161 3343 9148 8540 1130
## PGK1 8534 5477 11473 14256 4439
## LAMP2 4790 2275 3242 7064 1796
## CD40LG 35 2 39 78 22
## IRAK1 1784 1316 2605 3550 420
## COX2 34537 16093 16953 65836 114964. Selection of samples
Selection of samples related to psoriatic arthritis and ankylosing spondylitis from the dataset
# sample selection
gsms <- "XX2210102XX2102XX21021010200101010XXXXXXXX10XXXXXX10XX10102XX2XX2"
sml <- strsplit(gsms, split="")[[1]]
# Filter out excluded samples (marked as "X")
sel <- which(sml != "X")
sml <- sml[sel]
expr_data <- expr_data_filtered[, sel]
# group membership for samples
gs <- factor(sml)
groups <- make.names(c("non lesion PsA", "lesion PsA", "normal skin AS"))
levels(gs) <- groups
sample_info <- data.frame(Group = gs, row.names = colnames(expr_data))
dsa <- DESeqDataSetFromMatrix(countData=expr_data, colData=sample_info, design= ~Group)
dsb <- DESeq(dsa, test="LRT", reduced = ~ 1) # Use LRT for all-around gene ranking## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 2 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing# extract results for top genes table
r <- results(dsb, alpha=0.05, pAdjustMethod ="fdr")
plotDispEsts(dsb, main="GSE186063 Dispersion Estimates")
# create histogram plot of p-values
hist(r$padj, breaks=seq(0, 1, length = 21), col = "grey", border = "white",
xlab = "", ylab = "", main = "GSE186063 Frequencies of padj-values")
#After filtering which groups have some expressed genes
cts <- list(c("Group",groups[1],groups[2]),
c("Group",groups[3],groups[2]),
c("Group",groups[1],groups[3])) # contrasts of interest
# Wald test to obtain contrast-specific results
dsc <- DESeq(dsa, test = "Wald", sfType = "poscount")## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 2 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingr <- results (dsc, contrast=cts[[1]], alpha=0.05, pAdjustMethod = "fdr")
# Venn diagram
library(gplots)
all_res <- list()
for (ct in cts) {
i <- length(all_res)
r <- results(dsc, contrast=ct, alpha=0.05, pAdjustMethod = "fdr")
all_res[[i + 1]] <- rownames(r)[!is.na(r$padj) & r$padj < 0.05 & abs(r$log2FoldChange) >= 1.0 ]
names(all_res)[i + 1] <- paste(ct, collapse="_")
}
venn(all_res)
5. Differential gene expression analysis
# Differential gene expression analysis
df <- as.data.frame(r)
df <- na.omit(df)
topGenes <- rownames(df)
heatmapData <- expr_data[topGenes, ]
# Load the necessary libraries
library(pheatmap)
library(RColorBrewer)
# Calculate the average expression for each gene
media_dos_genes <- rowMeans(heatmapData)
# Sort the averages in descending order
media_ordenada <- sort(media_dos_genes, decreasing = TRUE)
# Get the names of the first 20 genes
top_20_genes_por_media <- names(head(media_ordenada, 20))
# Filter the original matrix to retain only the 20 selected genes
heatmapData_top20 <- heatmapData[top_20_genes_por_media, ]
# Create column annotation with groups
annotation_data <- data.frame(
group = sample_info$Group,
row.names = rownames(sample_info)
)
# Reorder columns by group
ordem_grupo <- order(annotation_data$group) # Ordena por nome do grupo (alfabética)
heatmapData_top20 <- heatmapData_top20[, ordem_grupo]
annotation_data <- annotation_data[ordem_grupo, , drop = FALSE]
# Read the libraries below
library(ComplexHeatmap)
library(circlize)
# Matrix of the 20 genes
mat <- heatmapData_top20
# Reschedule by line
mat_scaled <- t(scale(t(mat)))
# Create group annotation
ha_col <- HeatmapAnnotation(
Group = annotation_data$group,
col = list(Group = c("lesion.PsA" = "#d62728",
"non.lesion.PsA" = "#1f77b4",
"normal.skin.AS" = "green"))
)
# Inverted RdBu colour palette
cores <- colorRamp2(c(-2, 0, 2), rev(RColorBrewer::brewer.pal(3, "RdBu")))
# Heatmap with clustering within groups
Heatmap(
mat_scaled,
name = "Z-score",
top_annotation = ha_col,
col = cores,
cluster_columns = TRUE,
cluster_column_slices = TRUE, # << cluster within each group
column_split = annotation_data$group, # << separate the columns by group
cluster_rows = TRUE,
show_column_names = FALSE,
show_row_names = TRUE,
column_title = "Heatmap of the 20 most highly expressed genes (average) from GSE186063",
heatmap_legend_param = list(title = "Z-score")
)
# UMAP plot (multi-dimensional scaling)
expr_data_umap <- expr_data[rowSums(expr_data) > 0, ] # Remove rows with zero sum
u <- umap(t(expr_data_umap), n_neighbors=15, random_state=123)
plot(u$layout, main="GSE186063 UMAP", xlab="", ylab="", tcl=0.1, pch=19, col="blue")
text(u$layout, labels=colnames(expr_data_umap), cex=0.7, pos=3)
# Assuming that rownames(df) are gene symbols
gene_symbols <- rownames(df)
# Convert gene symbols to Entrez IDs
gene_entrez_ids <- bitr(gene_symbols, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)## 'select()' returned 1:1 mapping between keys and columns# First, convert the rownames from df into a column
df$Symbol <- rownames(df)
# Join the original table and the converted IDs
dfd <- merge(df, gene_entrez_ids, by.x = "Symbol", by.y = "SYMBOL", all.x = TRUE)
# Ensure unique symbols
dfd <- dfd[!duplicated(dfd$ENTREZID), ]6. Comparison between skin samples from non-lesions of psoriatic arthritis versus lesions of psoriatic arthritis
# Wald test to obtain contrast-specific results
dsd <- DESeq(dsa, test = "Wald", sfType = "poscount")## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 2 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingr <- results(dsd, contrast = c("Group", groups[1], groups[2]), alpha = 0.05, pAdjustMethod = "fdr")
# Filter only significant genes
sig_genes <- subset(r, padj < 0.05 & abs(log2FoldChange) >= 1)
# volcano plot
old.pal <- palette(c("#00BFFF", "#FF3030")) # low-hi colors
par(mar=c(4,4,2,1), cex.main=1.5)
with(sig_genes, {
plot(log2FoldChange, -log10(padj), main=paste(groups[1], "vs", groups[2]),
xlab="log2FC", ylab="-log10(Padj)", pch=20, cex=0.5)
text(log2FoldChange, -log10(padj), labels=rownames(r), cex=0.6, pos=4)
})
with(subset(r, padj<0.05 & abs(log2FoldChange) >= 1),
points(log2FoldChange, -log10(padj), pch=20, col=(sign(log2FoldChange) + 3)/2, cex=1))
legend("bottomleft", title=paste("Padj<", 0.05, sep=""), legend=c("down", "up"), pch=20,col=1:2)
# Plot only significant genes
#MD PLOT
par(mar=c(4,4,2,1), cex.main=1.5)
with(sig_genes, {
plot(log10(baseMean), log2FoldChange,
main=paste(groups[1], "vs", groups[2]),
xlab="log10(mean of normalized counts)", ylab="log2FoldChange",
pch=20, col=(sign(log2FoldChange) + 3)/2, cex=1)
text(log10(baseMean), log2FoldChange, labels=rownames(sig_genes), cex=0.6, pos=4)
})
legend("bottomleft", title=paste("Padj<", 0.05, sep=""), legend=c("down", "up"), pch=20, col=1:2)
abline(h=0)
palette(old.pal) # restaurar paleta
# Add the gene symbols to the points on the graph.
library(ggplot2)
library(ggrepel)
plotVolcano <- function(res, title = "Volcano Plot") {
res$group <- "NS"
res$group[res$padj < 0.05 & res$log2FoldChange > 1] <- "Up"
res$group[res$padj < 0.05 & res$log2FoldChange < -1] <- "Down"
res$label <- ifelse(res$group != "NS", rownames(res), NA)
ggplot(res, aes(x = log2FoldChange, y = -log10(padj), color = group)) +
geom_point(alpha = 0.7, size = 2) +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = c(-1, 1), linetype = "dashed") +
scale_color_manual(values = c("Up" = "firebrick", "Down" = "dodgerblue", "NS" = "grey80")) +
ggrepel::geom_text_repel(aes(label = label), size = 3, max.overlaps = Inf) +
theme_minimal() +
labs(title = title, x = "log2 Fold Change", y = "-log10 adjusted p-value", color = "Regulation")
}
# Example of function usage
plotVolcano(r, paste(groups[1], "vs", groups[2]))## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 308 rows containing missing values or values outside the scale range
## (`geom_text_repel()`).
# Genes upregulated
up_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange >= 1))
# Genes downregulated
down_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange <= -1))
# All DEGs with |log2FC| > 2
de_genes <- c(up_genes, down_genes)
# Display gene symbols before conversion
expr_datasa <- as.data.frame(expr_data[rownames(expr_data) %in% de_genes, ])
# Convert expression data to long format for ggplot2
expr_datas <- as.data.frame(expr_datasa)
expr_datas$Gene <- rownames(expr_datas)
expr_datas <- pivot_longer(expr_datas, cols = -Gene, names_to = "Sample", values_to = "Expression")
expr_datas <- merge(expr_datas, pheno_data, by.x = "Sample", by.y = "geo_accession")
# Check column names and a sample of the data
expresse <- r[order(r$padj)[1:429], ]
expresse <- merge(as.data.frame(expresse), expr_datasa, by = 0, sort = F)
library(dplyr)
library(tibble)
library(clusterProfiler)
library(ggplot2)
r_df <- as.data.frame(r)
# This ensures that gene names are preserved
r_df_com_genes <- r_df %>%
rownames_to_column(var = "Gene")
# --- Now, the rest of your code works perfectly ---
top30_fc <- r_df_com_genes %>%
# O 'filter' também pode ser conflitante, é bom usar o prefixo
dplyr::filter(padj < 0.05 & abs(log2FoldChange) > 1) %>%
dplyr::arrange(desc(abs(log2FoldChange))) %>%
# Esta é a correção principal:
dplyr::slice(1:30) %>%
dplyr::mutate(
Regulation = case_when(
log2FoldChange > 1 ~ "Upregulated",
log2FoldChange < -1 ~ "Downregulated"
)
)
# Graph with positive and negative bars (your code here is perfect)
ggplot(top30_fc, aes(x = reorder(Gene, log2FoldChange), y = log2FoldChange, fill = Regulation)) +
geom_col() +
coord_flip() + # Coloca os genes no eixo Y
scale_fill_manual(values = c("Upregulated" = "steelblue", "Downregulated" = "tomato")) +
scale_y_continuous(breaks = seq(-8, 4, by = 1)) + # Ajuste os limites se necessário
theme_bw() +
labs(
title = "The 30 most prominent DEGs between non-lesion and lesion PsA (GSE186063)",
x = "Gene",
y = "log2 Fold Change",
fill = "Regulation"
) +
theme(
axis.text.y = element_text(color = "black", face = "bold", size = 9)
)
# --- Select the 20 MOST overexpressed genes (Your code here is perfect) ---
up_20genes <- expresse %>%
filter(padj < 0.05 & log2FoldChange > 1) %>%
arrange(desc(log2FoldChange)) %>%
head(20) %>%
pull(Row.names)
# --- Filters expression data ---
expr_datas_filtrado_up <- expr_datas %>%
filter(`diagnosis:ch1` == "Psoriatic Arthritis") %>%
filter(`source_name_ch1` %in% c("non-lesion", "lesion")) %>%
filter(Gene %in% up_20genes)
# --- Calculate the average per gene and per group ---
expr_datasa_up <- expr_datas_filtrado_up %>%
group_by(Gene, `source_name_ch1`) %>%
summarise(
mean_expression = mean(Expression, na.rm = TRUE),
sd_expression = sd(Expression, na.rm = TRUE),
.groups = 'drop'
)
# --- Preparation for the chart ---
dados_para_plotar_up <- expr_datasa_up %>%
filter(source_name_ch1 == "non-lesion")
# --- Graph Generation ---
ggplot(dados_para_plotar_up, aes(x = mean_expression, y = reorder(Gene, mean_expression))) +
geom_col(fill = "steelblue", color = "black", width = 0.7) +
labs(
title = "Average Expression of the Main Upregulated Genes in Non-Lesion Skin", # Título mais claro
subtitle = "For non-lesion PsA vs lesion PsA (GSE186063)",
x = "Average Expression in the Group 'Non-Lesion'", # Rótulo do eixo mais claro
y = "Gene"
) +
theme_minimal(base_size = 14) +
theme(
panel.grid.major.y = element_blank(),
axis.text.y = element_text(size = 12)
)
# --- Selects the 20 MOST under-expressed genes ---
down_20genes <- expresse %>%
filter(padj < 0.05 & log2FoldChange < -1) %>%
arrange(log2FoldChange) %>% # <-- Sort from most negative to least negative
head(20) %>%
pull(Row.names)
# --- Filters expression data for genes and groups of interest ---
expr_datas_filtrado_down <- expr_datas %>%
filter(`diagnosis:ch1` == "Psoriatic Arthritis") %>%
filter(`source_name_ch1` %in% c("lesion", "non-lesion")) %>%
filter(Gene %in% down_20genes)
# --- Calculate the average per gene and per group ---
expr_datasa_down <- expr_datas_filtrado_down %>%
group_by(Gene, `source_name_ch1`) %>%
summarise(
mean_expression = mean(Expression, na.rm = TRUE),
.groups = 'drop'
)
# --- Preparation for the chart ---'
dados_para_plotar <- expr_datasa_down %>%
filter(source_name_ch1 == "non-lesion")
# --- Graph Generation ---
ggplot(dados_para_plotar, aes(x = mean_expression, y = reorder(Gene, -mean_expression))) +
geom_col(fill = "red", color = "black", width = 0.7) +
labs(
title = "Average Expression of the Main Downregulated Genes in Non-Lesion Skin", # Título mais claro
subtitle = "For non-lesion PsA vs lesion PsA (GSE186063)",
x = "Average Expression in the Group 'Non-Lesion'", # Rótulo do eixo mais claro
y = "Gene"
) +
theme_minimal(base_size = 14) +
theme(
panel.grid.major.y = element_blank(),
axis.text.y = element_text(size = 12)
)
# --- Create 'count_matrix' ---
up_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange > 1))
down_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange < -1))
genes_interesse <- c(up_genes, down_genes)
expr_filtrado <- expr_datas %>%
filter(`diagnosis:ch1` == "Psoriatic Arthritis") %>%
filter(`source_name_ch1` %in% c("non-lesion", "lesion")) %>%
filter(Gene %in% genes_interesse)
matriz_contagem <- expr_filtrado %>%
select(Gene, Sample, Expression) %>%
pivot_wider(names_from = Sample, values_from = Expression, values_fill = 0) %>%
column_to_rownames(var = "Gene")
# Filter your original “sample_info” object to keep only the samples that are in your final matrix.
# This ensures that the annotation corresponds exactly to the heatmap data.
sample_info_filtrado <- subset(sample_info, rownames(sample_info) %in% colnames(matriz_contagem))
# Select the 20 most highly expressed genes
media_dos_genes <- rowMeans(matriz_contagem)
media_ordenada <- sort(media_dos_genes, decreasing = TRUE)
top_20_genes_por_media <- names(head(media_ordenada, 20))
heatmapData_top20 <- as.matrix(matriz_contagem[top_20_genes_por_media, ])
# Create the annotation for the heatmap from the already filtered information.
annotation_data <- data.frame(
Group = sample_info_filtrado$Group,
row.names = rownames(sample_info_filtrado)
)
# Ensure that the order of the columns in the heatmap and annotation is the same.
heatmapData_top20 <- heatmapData_top20[, rownames(annotation_data)]
# --- Heatmap generation with ComplexHeatmap ---
library(ComplexHeatmap)
library(circlize)
library(RColorBrewer)
# Rescale data by row (Z-score)
mat_scaled <- t(scale(t(heatmapData_top20)))
# Create the column annotation
ha_col <- HeatmapAnnotation(
Group = annotation_data$Group,
col = list(Group = c("non.lesion.PsA" = "#1f77b4", "lesion.PsA" = "#d62728"))
)
# Define the colour palette
cores <- colorRamp2(c(-2, 0, 2), rev(RColorBrewer::brewer.pal(3, "RdBu")))
# Generate the heatmap
Heatmap(
mat_scaled,
name = "Z-score",
top_annotation = ha_col,
col = cores,
# Divide the columns into groups for clearer viewing
column_split = annotation_data$Group,
cluster_rows = TRUE,
cluster_columns = TRUE, # Cluster within each divided group
show_column_names = FALSE,
show_row_names = TRUE,
column_title = "Heatmap of the 20 most highly expressed DEGs (non-lesion vs. lesion PsA GSE186063)",
heatmap_legend_param = list(title = "Z-score")
)
# Filter differentially expressed genes
de_genes <- subset(r, padj < 0.05 & abs(log2FoldChange) > 1)
# Create a data frame from de_genes
de_genes <- data.frame(de_genes)
# Suppose your dataframe is called df and you want to select columns ‘column1’, “column2” and ‘column3’.
de_genes <- de_genes %>% select(log2FoldChange, padj)
# Assuming that rownames(df) are gene symbols
gene_symbols <- rownames(de_genes)
# Convert gene symbols to Entrez IDs
gene_entrez_ids <- bitr(gene_symbols, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)## 'select()' returned 1:1 mapping between keys and columns# First, convert the rownames from df into a column
de_genes$Symbol <- rownames(de_genes)
# Join the original table and the converted IDs
de_genes <- merge(de_genes, gene_entrez_ids, by.x = "Symbol", by.y = "SYMBOL", all.x = TRUE)
# Ensure unique symbols
de_genes <- de_genes[!duplicated(de_genes$ENTREZID), ]
# View the first results to verify the addition of the IDs
de_genes <- de_genes %>%
filter(Symbol %in% rownames(matriz_contagem))
BiocManager::install("GOSemSim")## 'getOption("repos")' replaces Bioconductor standard repositories, see
## 'help("repositories", package = "BiocManager")' for details.
## Replacement repositories:
## CRAN: https://p3m.dev/cran/__linux__/jammy/latest## Bioconductor version 3.20 (BiocManager 1.30.26), R 4.4.1 (2024-06-14)## Installing package(s) 'GOSemSim'## Warning in install.packages(...): installation of package 'GOSemSim' had
## non-zero exit status## Installation paths not writeable, unable to update packages
## path: /usr/local/lib/R/library
## packages:
## boot, class, cluster, foreign, KernSmooth, lattice, MASS, Matrix, mgcv,
## nlme, nnet, rpart, spatial, survival
## path: /usr/local/lib/R/site-library
## packages:
## annotate, AnnotationDbi, ape, aplot, askpass, BH, Biobase, BiocFileCache,
## BiocGenerics, BiocManager, BiocParallel, BiocVersion, biomaRt, Biostrings,
## bit, bit64, bitops, Boruta, broom, bslib, C50, car, caret, checkmate, chk,
## classInt, cli, clock, clue, clusterProfiler, colorspace, commonmark,
## ComplexHeatmap, corrplot, cowplot, cpp11, credentials, crosstalk, Cubist,
## cutpointr, dbplyr, dbscan, DelayedArray, DEoptimR, Deriv, DESeq2, devtools,
## DiceKriging, diffobj, doBy, docopt, DOSE, dotCall64, downloader, DT,
## dtplyr, edgeR, enrichplot, entropy, evaluate, fastDummies, fastmatch,
## fgsea, fitdistrplus, fontawesome, forcats, fs, future, future.apply,
## gargle, GDCRNATools, genefilter, generics, GenomeInfoDb, GenomeInfoDbData,
## GenomicDataCommons, GenomicRanges, gert, ggforce, ggfun, ggnewscale,
## ggplot2, ggplotify, ggpubr, ggraph, ggridges, ggsci, ggtree, gh, gld,
## glmnet, globals, glue, GO.db, googledrive, googlesheets4, GOSemSim, gower,
## GPArotation, gplots, graph, graphlayouts, gtable, hardhat, haven, HDO.db,
## here, hms, httpuv, httr2, IRanges, jpeg, jsonlite, KEGGgraph, KEGGREST,
## keras, KMsurv, knitr, labelled, later, lava, lavaan, lgr, limma, littler,
## lme4, lmom, locfit, lubridate, magrittr, markdown, MatchIt, MatrixGenerics,
## MatrixModels, matrixStats, maxstat, mice, mime, miniUI, mlbench, mlr3,
## mlr3learners, mlr3measures, mlr3misc, mlr3pipelines, modeltools, multcomp,
## mvtnorm, networkD3, nloptr, org.Hs.eg.db, party, partykit, patchwork,
## pathview, pbapply, pbkrtest, pillar, pkgbuild, pkgdown, pkgload, plotly,
## pROC, processx, prodlim, progressr, PRROC, ps, psych, purrr, quantmod,
## quantreg, questionr, qvalue, R.cache, R.oo, R.utils, R6, ragg, ranger,
## Rcpp, RcppArmadillo, RcppTOML, RCurl, readxl, recipes, reshape, rgl,
## Rgraphviz, rlang, rmarkdown, robustbase, roxygen2, rprojroot, rsq, rstatix,
## rstudioapi, rversions, rvest, S4Arrays, S4Vectors, sass, scales,
## scatterpie, sctransform, sessioninfo, Seurat, SeuratObject, shadowtext,
## shiny, sp, spam, SparseArray, spatstat.data, spatstat.explore,
## spatstat.geom, spatstat.random, spatstat.univar, spatstat.utils, statmod,
## stringi, stringr, styler, SummarizedExperiment, survminer, sva, svglite,
## sys, systemfonts, TCGAbiolinks, TCGAbiolinksGUI.data, tensor, tensorflow,
## testthat, textshaping, tfruns, TH.data, tibble, timeDate, tinytex, treeio,
## tzdb, UCSC.utils, usethis, utf8, uwot, VIM, vroom, waldo, withr, xgboost,
## XML, xml2, xts, XVector, yulab.utils, zeallot, zip, zlibbioc, zoo## Old packages: 'curl', 'data.table', 'Hmisc', 'igraph', 'openssl', 'parallelly',
## 'promises', 'reticulate', 'RSQLite', 'xfun'library(GOSemSim)
BiocManager::install("enrichplot")## 'getOption("repos")' replaces Bioconductor standard repositories, see
## 'help("repositories", package = "BiocManager")' for details.
## Replacement repositories:
## CRAN: https://p3m.dev/cran/__linux__/jammy/latest## Bioconductor version 3.20 (BiocManager 1.30.26), R 4.4.1 (2024-06-14)## Installing package(s) 'enrichplot'## also installing the dependencies 'DOSE', 'GOSemSim'## Warning in install.packages(...): installation of package 'GOSemSim' had
## non-zero exit status## Warning in install.packages(...): installation of package 'DOSE' had non-zero
## exit status## Warning in install.packages(...): installation of package 'enrichplot' had
## non-zero exit status## Installation paths not writeable, unable to update packages
## path: /usr/local/lib/R/library
## packages:
## boot, class, cluster, foreign, KernSmooth, lattice, MASS, Matrix, mgcv,
## nlme, nnet, rpart, spatial, survival
## path: /usr/local/lib/R/site-library
## packages:
## annotate, AnnotationDbi, ape, aplot, askpass, BH, Biobase, BiocFileCache,
## BiocGenerics, BiocManager, BiocParallel, BiocVersion, biomaRt, Biostrings,
## bit, bit64, bitops, Boruta, broom, bslib, C50, car, caret, checkmate, chk,
## classInt, cli, clock, clue, clusterProfiler, colorspace, commonmark,
## ComplexHeatmap, corrplot, cowplot, cpp11, credentials, crosstalk, Cubist,
## cutpointr, dbplyr, dbscan, DelayedArray, DEoptimR, Deriv, DESeq2, devtools,
## DiceKriging, diffobj, doBy, docopt, DOSE, dotCall64, downloader, DT,
## dtplyr, edgeR, enrichplot, entropy, evaluate, fastDummies, fastmatch,
## fgsea, fitdistrplus, fontawesome, forcats, fs, future, future.apply,
## gargle, GDCRNATools, genefilter, generics, GenomeInfoDb, GenomeInfoDbData,
## GenomicDataCommons, GenomicRanges, gert, ggforce, ggfun, ggnewscale,
## ggplot2, ggplotify, ggpubr, ggraph, ggridges, ggsci, ggtree, gh, gld,
## glmnet, globals, glue, GO.db, googledrive, googlesheets4, GOSemSim, gower,
## GPArotation, gplots, graph, graphlayouts, gtable, hardhat, haven, HDO.db,
## here, hms, httpuv, httr2, IRanges, jpeg, jsonlite, KEGGgraph, KEGGREST,
## keras, KMsurv, knitr, labelled, later, lava, lavaan, lgr, limma, littler,
## lme4, lmom, locfit, lubridate, magrittr, markdown, MatchIt, MatrixGenerics,
## MatrixModels, matrixStats, maxstat, mice, mime, miniUI, mlbench, mlr3,
## mlr3learners, mlr3measures, mlr3misc, mlr3pipelines, modeltools, multcomp,
## mvtnorm, networkD3, nloptr, org.Hs.eg.db, party, partykit, patchwork,
## pathview, pbapply, pbkrtest, pillar, pkgbuild, pkgdown, pkgload, plotly,
## pROC, processx, prodlim, progressr, PRROC, ps, psych, purrr, quantmod,
## quantreg, questionr, qvalue, R.cache, R.oo, R.utils, R6, ragg, ranger,
## Rcpp, RcppArmadillo, RcppTOML, RCurl, readxl, recipes, reshape, rgl,
## Rgraphviz, rlang, rmarkdown, robustbase, roxygen2, rprojroot, rsq, rstatix,
## rstudioapi, rversions, rvest, S4Arrays, S4Vectors, sass, scales,
## scatterpie, sctransform, sessioninfo, Seurat, SeuratObject, shadowtext,
## shiny, sp, spam, SparseArray, spatstat.data, spatstat.explore,
## spatstat.geom, spatstat.random, spatstat.univar, spatstat.utils, statmod,
## stringi, stringr, styler, SummarizedExperiment, survminer, sva, svglite,
## sys, systemfonts, TCGAbiolinks, TCGAbiolinksGUI.data, tensor, tensorflow,
## testthat, textshaping, tfruns, TH.data, tibble, timeDate, tinytex, treeio,
## tzdb, UCSC.utils, usethis, utf8, uwot, VIM, vroom, waldo, withr, xgboost,
## XML, xml2, xts, XVector, yulab.utils, zeallot, zip, zlibbioc, zoo## Old packages: 'curl', 'data.table', 'Hmisc', 'igraph', 'openssl', 'parallelly',
## 'promises', 'reticulate', 'RSQLite', 'xfun'library(enrichplot)
BiocManager::install("STRINGdb")## 'getOption("repos")' replaces Bioconductor standard repositories, see
## 'help("repositories", package = "BiocManager")' for details.
## Replacement repositories:
## CRAN: https://p3m.dev/cran/__linux__/jammy/latest## Bioconductor version 3.20 (BiocManager 1.30.26), R 4.4.1 (2024-06-14)## Warning: package(s) not installed when version(s) same as or greater than current; use
## `force = TRUE` to re-install: 'STRINGdb'## Installation paths not writeable, unable to update packages
## path: /usr/local/lib/R/library
## packages:
## boot, class, cluster, foreign, KernSmooth, lattice, MASS, Matrix, mgcv,
## nlme, nnet, rpart, spatial, survival
## path: /usr/local/lib/R/site-library
## packages:
## annotate, AnnotationDbi, ape, aplot, askpass, BH, Biobase, BiocFileCache,
## BiocGenerics, BiocManager, BiocParallel, BiocVersion, biomaRt, Biostrings,
## bit, bit64, bitops, Boruta, broom, bslib, C50, car, caret, checkmate, chk,
## classInt, cli, clock, clue, clusterProfiler, colorspace, commonmark,
## ComplexHeatmap, corrplot, cowplot, cpp11, credentials, crosstalk, Cubist,
## cutpointr, dbplyr, dbscan, DelayedArray, DEoptimR, Deriv, DESeq2, devtools,
## DiceKriging, diffobj, doBy, docopt, DOSE, dotCall64, downloader, DT,
## dtplyr, edgeR, enrichplot, entropy, evaluate, fastDummies, fastmatch,
## fgsea, fitdistrplus, fontawesome, forcats, fs, future, future.apply,
## gargle, GDCRNATools, genefilter, generics, GenomeInfoDb, GenomeInfoDbData,
## GenomicDataCommons, GenomicRanges, gert, ggforce, ggfun, ggnewscale,
## ggplot2, ggplotify, ggpubr, ggraph, ggridges, ggsci, ggtree, gh, gld,
## glmnet, globals, glue, GO.db, googledrive, googlesheets4, GOSemSim, gower,
## GPArotation, gplots, graph, graphlayouts, gtable, hardhat, haven, HDO.db,
## here, hms, httpuv, httr2, IRanges, jpeg, jsonlite, KEGGgraph, KEGGREST,
## keras, KMsurv, knitr, labelled, later, lava, lavaan, lgr, limma, littler,
## lme4, lmom, locfit, lubridate, magrittr, markdown, MatchIt, MatrixGenerics,
## MatrixModels, matrixStats, maxstat, mice, mime, miniUI, mlbench, mlr3,
## mlr3learners, mlr3measures, mlr3misc, mlr3pipelines, modeltools, multcomp,
## mvtnorm, networkD3, nloptr, org.Hs.eg.db, party, partykit, patchwork,
## pathview, pbapply, pbkrtest, pillar, pkgbuild, pkgdown, pkgload, plotly,
## pROC, processx, prodlim, progressr, PRROC, ps, psych, purrr, quantmod,
## quantreg, questionr, qvalue, R.cache, R.oo, R.utils, R6, ragg, ranger,
## Rcpp, RcppArmadillo, RcppTOML, RCurl, readxl, recipes, reshape, rgl,
## Rgraphviz, rlang, rmarkdown, robustbase, roxygen2, rprojroot, rsq, rstatix,
## rstudioapi, rversions, rvest, S4Arrays, S4Vectors, sass, scales,
## scatterpie, sctransform, sessioninfo, Seurat, SeuratObject, shadowtext,
## shiny, sp, spam, SparseArray, spatstat.data, spatstat.explore,
## spatstat.geom, spatstat.random, spatstat.univar, spatstat.utils, statmod,
## stringi, stringr, styler, SummarizedExperiment, survminer, sva, svglite,
## sys, systemfonts, TCGAbiolinks, TCGAbiolinksGUI.data, tensor, tensorflow,
## testthat, textshaping, tfruns, TH.data, tibble, timeDate, tinytex, treeio,
## tzdb, UCSC.utils, usethis, utf8, uwot, VIM, vroom, waldo, withr, xgboost,
## XML, xml2, xts, XVector, yulab.utils, zeallot, zip, zlibbioc, zoo## Old packages: 'curl', 'data.table', 'Hmisc', 'igraph', 'openssl', 'parallelly',
## 'promises', 'reticulate', 'RSQLite', 'xfun'library(STRINGdb)
#Alternative Workflow with STRINGdb Integration in R
# Initialise the STRINGdb object for a specific organism
string_db <- STRINGdb$new(version = "12", species = 9606, score_threshold = 400, input_directory = "")
options(timeout = 600) # increases to 10 minutes
# Gene mapping using STRINGdb
mapped_genes <- string_db$map(de_genes, "Symbol", removeUnmappedRows = TRUE)## Warning: we couldn't map to STRING 1% of your identifiers# Recover interactions for mapped genes
interactions <- string_db$get_interactions(mapped_genes$STRING_id)
# Visualise the network using igraph or other visualisation tools
library(igraph)##
## Attaching package: 'igraph'## The following object is masked from 'package:tibble':
##
## as_data_frame## The following objects are masked from 'package:rtracklayer':
##
## blocks, path## The following object is masked from 'package:BiocIO':
##
## path## The following object is masked from 'package:Biostrings':
##
## union## The following object is masked from 'package:XVector':
##
## path## The following object is masked from 'package:circlize':
##
## degree## The following objects are masked from 'package:topGO':
##
## algorithm, graph## The following objects are masked from 'package:graph':
##
## degree, edges, intersection, union## The following object is masked from 'package:clusterProfiler':
##
## simplify## The following object is masked from 'package:tidyr':
##
## crossing## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following object is masked from 'package:GenomicRanges':
##
## union## The following object is masked from 'package:IRanges':
##
## union## The following object is masked from 'package:S4Vectors':
##
## union## The following objects are masked from 'package:BiocGenerics':
##
## normalize, path, union## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## uniong <- graph_from_data_frame(interactions, directed=FALSE)
plot(g)
# Detect clusters (e.g. Louvain)
clusters <- cluster_louvain(g)
# Plot with colours by cluster
plot(g, vertex.color=clusters$membership)
# Perform GO enrichment analysis
# Biological Process
# Ensure you have the packages installed
library(clusterProfiler)
library(org.Hs.eg.db)
library(ggplot2)
library(dplyr) # For data manipulation
# Biological Process
enrich_result <- enrichGO(gene = mapped_genes$Symbol,
OrgDb = org.Hs.eg.db,
keyType = "SYMBOL",
ont = "BP", # Biological Ontology. It can be ‘BP’, “MF”, or ‘CC’
pAdjustMethod = "BH", # Benjamini-Hochberg (BH) is the same as FDR.
pvalueCutoff = 0.05,
qvalueCutoff = 0.2)
if (is.null(enrich_result) || nrow(as.data.frame(enrich_result)) == 0) {
print("No significantly enriched GO terms were found with the provided criteria.")
} else {
plot_data <- as.data.frame(enrich_result) %>%
arrange(p.adjust) %>%
head(15) # Display the 15 most significant terms
# Create a numeric column for the Y axis to use with geom_segment
# The order of the terms on the Y axis is important.
plot_data$y_pos <- rev(seq_along(plot_data$Description)) # To sort from top to bottom
ggplot(plot_data, aes(x = Count, y = y_pos)) + # Use y_pos for the Y axis
# Add geom_segment for the ‘threads’
# It starts at x=0 (or a small value) and goes up to the Count of the term.
geom_segment(aes(x = 0, xend = Count, y = y_pos, yend = y_pos),
color = "gray", linewidth = 0.5) +
geom_point(aes(size = Count, color = p.adjust), alpha = 0.8) +
scale_color_gradientn(
colors = c("darkblue", "steelblue", "lightblue", "lightgreen", "yellow", "orange", "darkred"),
name = "FDR",
trans = "log10",
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
) +
scale_size_area(max_size = 15, name = "Contagem de Genes") +
# Map labels back to GO descriptions
scale_y_continuous(breaks = plot_data$y_pos, labels = plot_data$Description) +
labs(
title = "GO Enrichment Analysis (Biological Processes)",
x = "Genes Counting",
y = "GO Terms (Biological Processes)"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 14, face = "bold"),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
legend.position = "right",
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10),
panel.grid.major.y = element_blank(), # Remove the default horizontal grid lines
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, linewidth=1)
)
}
barplot(enrich_result, showCategory = 10, title= "GO Enrichment Analysis")## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the enrichplot package.
## Please report the issue at
## <https://github.com/GuangchuangYu/enrichplot/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Network graph of enriched terms
cnetplot(enrich_result, showCategory = 10)## Warning: `aes_()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`
## ℹ The deprecated feature was likely used in the enrichplot package.
## Please report the issue at
## <https://github.com/GuangchuangYu/enrichplot/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Heatmap
heatplot(enrich_result, showCategory = 10)
# Perform KEGG enrichment analysis
ekegg <- enrichKEGG(gene = mapped_genes$ENTREZID, organism = "hsa", pAdjustMethod = "BH", qvalueCutoff = 0.05)## Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...## Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...# Generate GO/STRING style bubble chart
if (is.null(ekegg) || nrow(as.data.frame(ekegg)) == 0) {
print("No significantly enriched KEGG pathways were found with the criteria provided.")
} else {
plot_data_kegg <- as.data.frame(ekegg) %>%
arrange(p.adjust) %>% # Sort by p.adjust (FDR)
head(15) # Selects the 15 most significant pathways to plot
# Creates a numerical position for the terms on the Y-axis, to control the order and lines
plot_data_kegg$y_pos <- rev(seq_along(plot_data_kegg$Description)) # reverse to the most significant at the top
# Starting to build the graph with ggplot2
ggplot(plot_data_kegg, aes(x = Count, y = y_pos)) +
# Adds the connection lines (wires) from the Y-axis to the bubble.
geom_segment(aes(x = 0, xend = Count, y = y_pos, yend = y_pos),
color = "gray80", linewidth = 0.6) + # Softer colour for the lines
# Add the bubbles
geom_point(aes(size = Count, color = p.adjust), alpha = 0.9) +
# Colour scale configuration for FDR (p.adjust)
scale_color_gradientn(
colors = c("darkblue", "steelblue", "lightblue", "lightgreen", "yellow", "orange", "darkred"),
name = "FDR",
trans = "log10", # Use logarithmic scale for FDR
breaks = scales::trans_breaks("log10", function(x) 10^x), # Breaks in scientific notation captions
labels = scales::trans_format("log10", scales::math_format(10^.x)), # Scientific notation format
limits = c(min(plot_data_kegg$p.adjust), max(plot_data_kegg$p.adjust)) # Sets limits for data
) +
# Size scale configuration for Gene Counting
scale_size_area(
max_size = 18, # Maximum bubble size
name = "Gene count", # Caption title size
breaks = unique(round(quantile(plot_data_kegg$Count, probs = c(0.25, 0.5, 0.75, 1.0)))), # Suggests reasonable breaks
labels = unique(round(quantile(plot_data_kegg$Count, probs = c(0.25, 0.5, 0.75, 1.0))))
) +
# Maps the Y-axis labels back to the KEGG pathway descriptions
scale_y_continuous(
breaks = plot_data_kegg$y_pos,
labels = plot_data_kegg$Description
) +
# Labels and Chart Title
labs(
title = "KEGG Pathway Enrichment Analysis",
x = "Gene count", # X-axis is now Gene Count
y = "KEGG Pathway" # Y-axis is the name of the pathway
) +
# Chart Theme and Style
theme_minimal() +
theme(
axis.text.y = element_text(size = 12, face = "bold"), # KEGG terms in bold and larger font
axis.title = element_text(size = 14, face = "bold"), # Axis titles
plot.title = element_text(size = 16, face = "bold", hjust = 0.5), # Centred title
legend.position = "right", # Position of subtitles
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10),
panel.grid.major.y = element_blank(), # Remove standard horizontal grid lines
panel.grid.minor = element_blank(), # Remove minor grid liness
panel.border = element_rect(colour = "black", fill=NA, linewidth=1.2) # Black border around the plot
)
} 
barplot(ekegg, showCategory = 10, title = "KEGG Enrichment Analysis")## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?
# Network graph of enriched terms
cnetplot(ekegg, showCategory = 10)
# Heatmap
heatplot(ekegg, showCategory = 10)
7.Transcription factors in the analysis between non-lesion PsA vs lesion PsA
# Load the necessary packages
library(GenomicFeatures)##
## Attaching package: 'GenomicFeatures'## The following object is masked from 'package:topGO':
##
## geneslibrary(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(org.Hs.eg.db)
library(JASPAR2020)
library(TFBSTools)
library(SummarizedExperiment)
library(motifmatchr)
# Load the TxDb database
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
# Extraction of exons by gene
exons_by_gene <- exonsBy(txdb, by = "gene")
# Obtain the coordinates of the genes (taking the first and last positions of the exons)
genes_info <- range(exons_by_gene)
# Obtain transcription information
transcripts_info <- transcripts(txdb)
# Map genes of interest to Entrez IDs (replace “de_genes” with the actual list of genes)
gene_entrez <- mapIds(org.Hs.eg.db, keys = mapped_genes$Symbol, column = "ENTREZID", keytype = "SYMBOL", multiVals = "first")## 'select()' returned 1:1 mapping between keys and columns# Filter the genes of interest
promoters_info <- subset(genes_info, names(genes_info) %in% gene_entrez)
# Define promoter regions (2 kb upstream of TSS)
promoters <- promoters(promoters_info, upstream = 2000, downstream = 0)## Warning in valid.GenomicRanges.seqinfo(x, suggest.trim = TRUE): GRanges object contains 1 out-of-bound range located on sequence
## chr2_GL383522v1_alt. Note that ranges located on a sequence whose
## length is unknown (NA) or on a circular sequence are not considered
## out-of-bound (use seqlengths() and isCircular() to get the lengths and
## circularity flags of the underlying sequences). You can use trim() to
## trim these ranges. See ?`trim,GenomicRanges-method` for more
## information.# Filter the main chromosomes
promoters <- keepStandardChromosomes(promoters, pruning.mode = "coarse")
# Remove any regions that exceed the limits of the chromosomes.
promoters <- trim(promoters)
# Convert CompressedGRangesList promoters to GRanges
promoters_gr <- unlist(promoters)
# Load the JASPAR grounds database
motifs <- getMatrixSet(JASPAR2020, opts = list(species = "Homo sapiens"))
# Create a data frame with the IDs of the reasons and their names (TFs)
motif_info <- data.frame(
motif_id = names(motifs),
tf_name = sapply(motifs, function(x) x@name),
stringsAsFactors = FALSE
)
# Enriching motifs in promoting regions
motifHits <- matchMotifs(motifs, promoters_gr, genome = BSgenome.Hsapiens.UCSC.hg38)
# Count the number of occurrences of the reasons in each promoting region.
motif_counts <- countOverlaps(promoters_gr, motifHits)
# Add counts to GRanges with promoting regions
promoters_gr$motif_counts <- motif_counts
# Summary of motif counts
summary(promoters_gr$motif_counts)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4825 1.0000 1.0000# Obtain the names of the motifs and TFs
motif_names <- names(motifHits)
# Extract the reason table from the RangedSummarizedExperiment object
motif_data <- assays(motifHits)[[1]] # Assuming that the table of reasons is in the first list of assays
# Obtain information about TFs
# The names of TFs may be in the colnames or in associated metadata
tf_names <- colnames(motif_data)
# Add the reason count if available
motif_counts <- rowSums(motif_data)
# Check the dimensions of the motif_data object
dim(motif_data) # Number of rows and columns## [1] 114 633# Check the length of tf_names and motif_counts
tf_names_length <- length(tf_names)
motif_counts_length <- length(motif_counts)
# Print the lengths for diagnosis
cat("Length of tf_names:", tf_names_length, "\n")## Length of tf_names: 633cat("Length of motif_counts:", motif_counts_length, "\n")## Length of motif_counts: 114# Verify that the dimensions of motif_data correspond to the number of TFs and motifs
motif_data_dims <- dim(motif_data)
cat("Dimensions of motif_data (rows, columns):", motif_data_dims, "\n")## Dimensions of motif_data (rows, columns): 114 633# Check whether the number of reasons is greater or lesser
if (tf_names_length > motif_counts_length) {
# Check additional columns
tf_names <- tf_names[1:motif_counts_length] # Adjust to the length of motif_counts
} else {
# Adjust motif_counts to match tf_names
motif_counts <- motif_counts[1:tf_names_length]
}
# Now, create the data frame with adjusted lengths
motif_summary <- data.frame(
tf_name = tf_names,
motif_count = motif_counts
)
# Check the correspondence between TF names and the columns in motif_data
all(tf_names %in% colnames(motif_data)) # Should return TRUE if all TF names are present## [1] TRUE# Sort and identify key TFs
tf_summary <- motif_summary[order(motif_summary$motif_count, decreasing = TRUE), ]
top_tf_summary <- head(tf_summary, 121) # Adjust the number as required
# Mapping Entrez IDs to gene symbols
gene_symbols <- mapIds(org.Hs.eg.db, keys = rownames(top_tf_summary), column = "SYMBOL", keytype = "ENTREZID", multiVals = "first")## 'select()' returned 1:1 mapping between keys and columns# Replace rownames with gene symbols
rownames(top_tf_summary) <- gene_symbols
# Add reason ID as a column
top_tf_summary$motif_id <- rownames(top_tf_summary)
# Add to the table of TF names
motif1 <- merge(top_tf_summary, motif_info, by.x = "tf_name", by.y = "motif_id")
# Reorder columns
motif2 <- motif1[, c("motif_id", "tf_name.y", "motif_count")]
colnames(motif2) <- c("motif_id", "tf_name", "motif_count")
# Sort by frequency (motif_count) in descending order
motif2 <- motif2 %>% arrange(desc(motif_count))
print(motif2)## motif_id tf_name motif_count
## 1 CCL20 IRF8 236
## 2 CXCL8 YY1 204
## 3 CCR7 RREB1 196
## 4 CCND1 GSC 181
## 5 GJB6 MZF1(var.2) 176
## 6 CXCL2 POU2F2 173
## 7 PI3 ELF5 170
## 8 IL5 STAT3 168
## 9 FUT2 FOS 167
## 10 IFI6 FOSL2 167
## 11 NMI NKX2-3 166
## 12 PRF1 FOXL1 165
## 13 DKK1 RARA::RXRA 165
## 14 TNFRSF9 SREBF2 164
## 15 GZMA STAT1::STAT2 163
## 16 TGFBR3 MLXIPL 161
## 17 ZNF316 FOXF2 160
## 18 FCGR3A INSM1 159
## 19 PINK1 LHX6 159
## 20 SAMD9 ETV6 158
## 21 EFCAB13 SOX9 157
## 22 MEFV NFATC3 157
## 23 PLA2G4D MAF::NFE2 154
## 24 PLIN5 SHOX 154
## 25 PPARG EN2 154
## 26 RORC HEY2 154
## 27 IL12B SREBF1 152
## 28 PRDM1 IRF9 152
## 29 FAXDC2 MAX::MYC 150
## 30 WNK1 MEOX1 150
## 31 MUCL1 RORA(var.2) 149
## 32 IL17F PBX1 148
## 33 SLC7A11 ARNT::HIF1A 146
## 34 APOL6 NEUROD2 146
## 35 IL6 GATA1::TAL1 145
## 36 SERPINB1 EWSR1-FLI1 145
## 37 IRS1 DMRT3 143
## 38 IL37 MEF2C 140
## 39 OLR1 CENPB 140
## 40 NOD2 JDP2(var.2) 140
## 41 CTLA4 SRY 139
## 42 RSAD2 NKX2-8 139
## 43 GJB2 JUND(var.2) 138
## 44 AKAP13 PAX6 136
## 45 LYZ LBX1 136
## 46 SYT1 MLX 136
## 47 S100A8 HINFP 135
## 48 IL2RA STAT1 135
## 49 IL36RN JUN(var.2) 135
## 50 JRKL NFIX 135
## 51 PPARGC1A PPARG 134
## 52 GBP5 RORA 134
## 53 IL17A THAP1 134
## 54 IL21 GRHL1 134
## 55 CRB1 NR4A2 133
## 56 MMP1 POU6F1 133
## 57 XAF1 ESX1 133
## 58 HYAL4 PLAG1 132
## 59 NT5C3A DBP 132
## 60 ALB REST 131
## 61 EGR3 CTCF 130
## 62 IL23A ELF4 130
## 63 NOS2 BHLHE41 129
## 64 PTGS2 GCM1 129
## 65 APOL1 NFIA 129
## 66 CYP1A1 TAL1::TCF3 128
## 67 GATA3 FOXH1 128
## 68 IL22 CREB3 128
## 69 SELL ISX 128
## 70 CENPK JDP2 128
## 71 YOD1 FOXI1 126
## 72 PLG EN1 125
## 73 EGF NFIC::TLX1 125
## 74 ZNF415 FOXO3 125
## 75 CD274 NRF1 125
## 76 GZMB TCF7L2 125
## 77 CAPS2 NEUROG2 125
## 78 HCAR3 NHLH1 123
## 79 CD38 PAX4 122
## 80 GBP1 JUN 122
## 81 ICOS SMAD2::SMAD3::SMAD4 122
## 82 CXCR2 FOXP2 122
## 83 TYMP NR1H2::RXRA 121
## 84 FRZB DUX4 121
## 85 ACP7 FOXG1 121
## 86 NKG7 BARHL2 121
## 87 ACKR2 RXRA::VDR 120
## 88 GPT NR2C2 120
## 89 FOSL1 MYF6 120
## 90 IL1B IRF1 119
## 91 HERC6 FLI1 119
## 92 KANK4 RELA 117
## 93 S100A12 HNF1B 117
## 94 NAMPT FOXD1 116
## 95 CALD1 MSC 116
## 96 IFIT3 ELK4 115
## 97 IFNG ESR2 115
## 98 CXCL10 KLF5 114
## 99 ZC3H12A MSX1 114
## 100 MX1 ALX3 113
## 101 FCGR1A NFATC2 112
## 102 ZNF483 REL 111
## 103 S100A9 HNF1A 109
## 104 CSMD1 KLF13 109
## 105 STAT1 MIXL1 107
## 106 S100P HOXC11 105
## 107 SLC1A2 MEF2B 104
## 108 CD8A NKX6-1 103
## 109 CXCL13 IRF2 102
## 110 CCL2 HSF1 102
## 111 CLEC4D ZBTB33 101
## 112 AIM2 OLIG2 100
## 113 EGFR ZNF354C 90
## 114 CD80 NKX6-2 868. Comparison between normal skin in ankylosing spondylitis versus damaged skin in psoriatic arthritis
# Wald test to obtain contrast-specific results
dsd <- DESeq(dsa, test = "Wald", sfType = "poscount")## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 2 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingr <- results(dsd, contrast = c("Group", groups[3], groups[2]), alpha = 0.05, pAdjustMethod = "fdr")
groups <- levels(colData(dsd)$Group)
df <- as.data.frame(r)
# Filter only significant genes
sig_genes <- subset(r, padj < 0.05 & abs(log2FoldChange) >= 1)
# volcano plot
old.pal <- palette(c("#00BFFF", "#FF3030")) # low-hi colors
par(mar=c(4,4,2,1), cex.main=1.5)
with(sig_genes, {
plot(log2FoldChange, -log10(padj), main=paste(groups[3], "vs", groups[2]),
xlab="log2FC", ylab="-log10(Padj)", pch=20, cex=0.5)
text(log2FoldChange, -log10(padj), labels=rownames(r), cex=0.6, pos=4)
})
with(subset(r, padj<0.05 & abs(log2FoldChange) >= 1),
points(log2FoldChange, -log10(padj), pch=20, col=(sign(log2FoldChange) + 3)/2, cex=1))
legend("bottomleft", title=paste("Padj<", 0.05, sep=""), legend=c("down", "up"), pch=20,col=1:2)
# Plot only significant genes
# MD PLOT
par(mar=c(4,4,2,1), cex.main=1.5)
with(sig_genes, {
plot(log10(baseMean), log2FoldChange,
main=paste(groups[3], "vs", groups[2]),
xlab="log10(mean of normalized counts)", ylab="log2FoldChange",
pch=20, col=(sign(log2FoldChange) + 3)/2, cex=1)
text(log10(baseMean), log2FoldChange, labels=rownames(sig_genes), cex=0.6, pos=4)
})
legend("bottomleft", title=paste("Padj<", 0.05, sep=""), legend=c("down", "up"), pch=20, col=1:2)
abline(h=0)
palette(old.pal) # restore palette
# Add gene symbols to the points on the graph
# Install the packages if you do not already have them
library(ggplot2)
library(ggrepel)
plotVolcano <- function(res, title = "Volcano Plot") {
res$group <- "NS"
res$group[res$padj < 0.05 & res$log2FoldChange > 1] <- "Up"
res$group[res$padj < 0.05 & res$log2FoldChange < -1] <- "Down"
res$label <- ifelse(res$group != "NS", rownames(res), NA)
ggplot(res, aes(x = log2FoldChange, y = -log10(padj), color = group)) +
geom_point(alpha = 0.7, size = 2) +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = c(-1, 1), linetype = "dashed") +
scale_color_manual(values = c("Up" = "firebrick", "Down" = "dodgerblue", "NS" = "grey80")) +
ggrepel::geom_text_repel(aes(label = label), size = 3, max.overlaps = Inf) +
theme_minimal() +
labs(title = title, x = "log2 Fold Change", y = "-log10 adjusted p-value", color = "Regulation")
}
# Example of function usage
plotVolcano(r, paste(groups[3], "vs", groups[2]))## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 300 rows containing missing values or values outside the scale range
## (`geom_text_repel()`).
# Genes upregulated
up_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange >= 1))
# Genes downregulated
down_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange <= -1))
# All DEGs with |log2FC| > 2
de_genes <- c(up_genes, down_genes)
expr_datasa <- as.data.frame(expr_data[rownames(expr_data) %in% de_genes, ])
# Convert expression data to long format for ggplot2
expr_datas <- as.data.frame(expr_datasa)
expr_datas$Gene <- rownames(expr_datas)
expr_datas <- pivot_longer(expr_datas, cols = -Gene, names_to = "Sample", values_to = "Expression")
expr_datas <- merge(expr_datas, pheno_data, by.x = "Sample", by.y = "geo_accession")
# Check column names and a sample of the data
expresse <- r[order(r$padj)[1:429], ]
expresse <- merge(as.data.frame(expresse), expr_datasa, by = 0, sort = F)
library(dplyr)
library(tibble)
library(clusterProfiler)
library(ggplot2)
r_df <- as.data.frame(r)
# Assuming that “r_df” is your data frame with the DESeq2 results
# This ensures that gene names are preserved.
r_df_com_genes <- r_df %>%
rownames_to_column(var = "Gene")
top30_fc <- r_df_com_genes %>%
dplyr::filter(padj < 0.05 & abs(log2FoldChange) > 1) %>%
dplyr::arrange(desc(abs(log2FoldChange))) %>%
dplyr::slice(1:30) %>%
dplyr::mutate(
Regulation = case_when(
log2FoldChange > 1 ~ "Upregulated",
log2FoldChange < -1 ~ "Downregulated"
)
)
# Chart with positive and negative bars
ggplot(top30_fc, aes(x = reorder(Gene, log2FoldChange), y = log2FoldChange, fill = Regulation)) +
geom_col() +
coord_flip() + # Place the genes on the Y axis
scale_fill_manual(values = c("Upregulated" = "steelblue", "Downregulated" = "tomato")) +
scale_y_continuous(breaks = seq(-8, 4, by = 1)) + # Adjust the limits if necessary.
theme_bw() +
labs(
title = "The 30 most prominent DEGs between normal skin AS and lesion PsA (GSE186063)",
x = "Gene",
y = "log2 Fold Change",
fill = "Regulation"
) +
theme(
axis.text.y = element_text(color = "black", face = "bold", size = 9)
)
# --- Selects the 20 MOST overexpressed genes ---
up_20genes <- expresse %>%
filter(padj < 0.05 & log2FoldChange > 1) %>%
arrange(desc(log2FoldChange)) %>%
head(20) %>%
pull(Row.names)
# --- Filters expression data ---
expr_datas_filtrado_up <- expr_datas %>%
filter(`diagnosis:ch1` %in% c("Psoriatic Arthritis", "Ankylosing Spondylitis")) %>%
filter(`source_name_ch1` %in% c("lesion", "normal skin")) %>%
filter(Gene %in% up_20genes)
# --- Calculates the average per gene and per group ---
expr_datasa_up <- expr_datas_filtrado_up %>%
group_by(Gene, `source_name_ch1`) %>%
summarise(
mean_expression = mean(Expression, na.rm = TRUE),
sd_expression = sd(Expression, na.rm = TRUE),
.groups = 'drop'
)
# --- Preparation for the chart ---
# Filter to show the average expression ONLY in the “non-lesion” group
dados_para_plotar_up <- expr_datasa_up %>%
filter(source_name_ch1 == "normal skin")
# --- Graph Generation ---
ggplot(dados_para_plotar_up, aes(x = mean_expression, y = reorder(Gene, mean_expression))) +
geom_col(fill = "steelblue", color = "black", width = 0.7) +
labs(
title = "Average Expression of the Main Upregulated Genes in Normal Skin", # Clearer title
subtitle = "For normal skin AS vs lesion PsA (GSE186063)",
x = "Average Expression in the Group 'Normal skin'", # Clearer axle label
y = "Gene"
) +
theme_minimal(base_size = 14) +
theme(
panel.grid.major.y = element_blank(),
axis.text.y = element_text(size = 12)
)
# --- Selects the 20 MOST under-expressed genes ---
down_20genes <- expresse %>%
filter(padj < 0.05 & log2FoldChange < -1) %>%
arrange(log2FoldChange) %>% # <-- Sort from most negative to least negative
head(20) %>%
pull(Row.names)
# --- Filters expression data for genes and groups of interest ---
expr_datas_filtrado_down <- expr_datas %>%
filter(`diagnosis:ch1` %in% c("Psoriatic Arthritis", "Ankylosing Spondylitis")) %>%
filter(`source_name_ch1` %in% c("lesion", "normal skin")) %>%
filter(Gene %in% down_20genes)
# --- Calculates the average per gene and per group ---
expr_datasa_down <- expr_datas_filtrado_down %>%
group_by(Gene, `source_name_ch1`) %>%
summarise(
mean_expression = mean(Expression, na.rm = TRUE),
.groups = 'drop'
)
# --- Preparation for the chart ---
# Filter to show the average expression ONLY in the “lesion” group
dados_para_plotar <- expr_datasa_down %>%
filter(source_name_ch1 == "normal skin")
# --- Graph Generation ---
ggplot(dados_para_plotar, aes(x = mean_expression, y = reorder(Gene, -mean_expression))) +
geom_col(fill = "red", color = "black", width = 0.7) +
labs(
title = "Average Expression of the Main Downregulated Genes in Non-Lesion Skin", # Clearer title
subtitle = "For normal skin AS vs lesion PsA (GSE186063)",
x = "Average Expression in the Group 'Normal skin'", # Clearer axle label
y = "Gene"
) +
theme_minimal(base_size = 14) +
theme(
panel.grid.major.y = element_blank(),
axis.text.y = element_text(size = 12)
)
# --- The code to create 'count_array' ---
up_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange > 1))
down_genes <- rownames(subset(r, padj < 0.05 & log2FoldChange < -1))
genes_interesse <- c(up_genes, down_genes)
expr_filtrado <- expr_datas %>%
filter(`diagnosis:ch1` %in% c("Psoriatic Arthritis", "Ankylosing Spondylitis")) %>%
filter(`source_name_ch1` %in% c("lesion", "normal skin")) %>%
filter(Gene %in% genes_interesse)
matriz_contagem <- expr_filtrado %>%
select(Gene, Sample, Expression) %>%
pivot_wider(names_from = Sample, values_from = Expression, values_fill = 0) %>%
column_to_rownames(var = "Gene")
# Filter your original “sample_info” object to keep only the samples that are in your final matrix
# This ensures that the annotation corresponds exactly to the heatmap data
sample_info_filtrado <- subset(sample_info, rownames(sample_info) %in% colnames(matriz_contagem))
# Select the 20 most highly expressed genes
media_dos_genes <- rowMeans(matriz_contagem)
media_ordenada <- sort(media_dos_genes, decreasing = TRUE)
top_20_genes_por_media <- names(head(media_ordenada, 20))
heatmapData_top20 <- as.matrix(matriz_contagem[top_20_genes_por_media, ])
# Create the annotation for the heatmap from the filtered and corrected information
annotation_data <- data.frame(
Group = sample_info_filtrado$Group,
row.names = rownames(sample_info_filtrado)
)
# Ensure that the order of the columns in the heatmap and annotation is the same
heatmapData_top20 <- heatmapData_top20[, rownames(annotation_data)]
# --- Heatmap generation with ComplexHeatmap ---
# Rescale data by row (Z-score)
mat_scaled <- t(scale(t(heatmapData_top20)))
# Create the column annotation
ha_col <- HeatmapAnnotation(
Group = annotation_data$Group,
col = list(Group = c("normal.skin.AS" = "#1f77b4", "lesion.PsA" = "#d62728"))
)
# Define the colour palette
cores <- colorRamp2(c(-2, 0, 2), rev(RColorBrewer::brewer.pal(3, "RdBu")))
# Generate the heatmap
Heatmap(
mat_scaled,
name = "Z-score",
top_annotation = ha_col,
col = cores,
# Divide the columns into groups for clearer viewing
column_split = annotation_data$Group,
cluster_rows = TRUE,
cluster_columns = TRUE, # Cluster within each divided group
show_column_names = FALSE,
show_row_names = TRUE,
column_title = "Heatmap of the 20 most highly expressed DEGs (normal skin AS vs. lesion PsA GSE186063)",
heatmap_legend_param = list(title = "Z-score")
)
# Filter differentially expressed genes
de_genes <- subset(r, padj < 0.05 & abs(log2FoldChange) > 1)
#Create a data frame from de_genes
de_genes <- data.frame(de_genes)
# Suppose your dataframe is called df and you want to select columns ‘column1’, “column2” and ‘column3’
de_genes <- de_genes %>% select(log2FoldChange, padj)
# Assuming that rownames(df) are gene symbols
gene_symbols <- rownames(de_genes)
# Convert gene symbols to Entrez IDs
gene_entrez_ids <- bitr(gene_symbols, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)## 'select()' returned 1:1 mapping between keys and columns# First, convert the rownames from df into a column
de_genes$Symbol <- rownames(de_genes)
# Performs the join between the original table and the converted IDs
de_genes <- merge(de_genes, gene_entrez_ids, by.x = "Symbol", by.y = "SYMBOL", all.x = TRUE)
# Ensure unique symbols
de_genes <- de_genes[!duplicated(de_genes$ENTREZID), ]
# Alternative Workflow with STRINGdb Integration in R
# Initialise the STRINGdb object for a specific organism
string_db <- STRINGdb$new(version = "12", species = 9606, score_threshold = 400, input_directory = "")
options(timeout = 600) # increases to 10 minutes
# Gene mapping using STRINGdb
mapped_genes <- string_db$map(de_genes, "Symbol", removeUnmappedRows = TRUE)## Warning: we couldn't map to STRING 1% of your identifiers# Recover interactions for mapped genes
interactions <- string_db$get_interactions(mapped_genes$STRING_id)
# Visualise the network using igraph or other visualisation tools
library(igraph)
g <- graph_from_data_frame(interactions, directed=FALSE)
plot(g)
# Detect clusters (e.g. Louvain)
clusters <- cluster_louvain(g)
# Plot with colours by cluster
plot(g, vertex.color=clusters$membership)
# Perform GO enrichment analysis
# Biological Process
enrich_result <- enrichGO(gene = mapped_genes$Symbol,
OrgDb = org.Hs.eg.db,
keyType = "SYMBOL",
ont = "BP", # Biological Ontology. It can be ‘BP’, “MF”, or ‘CC’.
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.2)
barplot(enrich_result, showCategory = 10, title= "GO Enrichment Analysis")## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?
# Network graph of enriched terms
cnetplot(enrich_result, showCategory = 10)## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Heatmap
heatplot(enrich_result, showCategory = 10)
# Perform KEGG enrichment analysis
ekegg <- enrichKEGG(gene = mapped_genes$ENTREZID, organism = "hsa", pAdjustMethod = "BH", qvalueCutoff = 0.05)
# Generate GO/STRING style bubble chart
if (is.null(ekegg) || nrow(as.data.frame(ekegg)) == 0) {
print("No significantly enriched KEGG pathways were found with the criteria provided.")
} else {
plot_data_kegg <- as.data.frame(ekegg) %>%
arrange(p.adjust) %>% # Sort by p.adjust (FDR)
head(15) # Selects the 15 most significant pathways to plot
# Creates a numerical position for the terms on the Y-axis, to control the order and lines
plot_data_kegg$y_pos <- rev(seq_along(plot_data_kegg$Description)) # reverse to the most significant at the top
# Starting to build the graph with ggplot2
ggplot(plot_data_kegg, aes(x = Count, y = y_pos)) +
# Adds the connection lines (wires) from the Y-axis to the bubble
geom_segment(aes(x = 0, xend = Count, y = y_pos, yend = y_pos),
color = "gray80", linewidth = 0.6) + # Softer colour for the lines
# Add the bubbles
geom_point(aes(size = Count, color = p.adjust), alpha = 0.9) +
# Colour scale configuration for FDR (p.adjust)
scale_color_gradientn(
colors = c("darkblue", "steelblue", "lightblue", "lightgreen", "yellow", "orange", "darkred"),
name = "FDR",
trans = "log10", # Use logarithmic scale for FDR
breaks = scales::trans_breaks("log10", function(x) 10^x), # Breaks in scientific notation captions
labels = scales::trans_format("log10", scales::math_format(10^.x)), # Scientific notation format
limits = c(min(plot_data_kegg$p.adjust), max(plot_data_kegg$p.adjust)) # Sets limits for data
) +
# Size scale configuration for Gene Counting
scale_size_area(
max_size = 18, # Maximum bubble size
name = "Gene count", # Caption title size
breaks = unique(round(quantile(plot_data_kegg$Count, probs = c(0.25, 0.5, 0.75, 1.0)))), # Suggests reasonable breaks
labels = unique(round(quantile(plot_data_kegg$Count, probs = c(0.25, 0.5, 0.75, 1.0))))
) +
# Maps the Y-axis labels back to the KEGG pathway descriptions
scale_y_continuous(
breaks = plot_data_kegg$y_pos,
labels = plot_data_kegg$Description
) +
# Labels and Chart Title
labs(
title = "KEGG Pathway Enrichment Analysis",
x = "Gene count", # X-axis is now Gene Count
y = "KEGG Pathway" # Y-axis is the name of the pathway
) +
# Chart Theme and Style
theme_minimal() +
theme(
axis.text.y = element_text(size = 12, face = "bold"), # KEGG terms in bold and larger font
axis.title = element_text(size = 14, face = "bold"), # Axis titles
plot.title = element_text(size = 16, face = "bold", hjust = 0.5), # Centred title
legend.position = "right", # Position of subtitles
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10),
panel.grid.major.y = element_blank(), # Remove standard horizontal grid lines
panel.grid.minor = element_blank(), # Remove smaller grid lines
panel.border = element_rect(colour = "black", fill=NA, linewidth=1.2) # Black border around the plot
)
} 
barplot(ekegg, showCategory = 10, title = "KEGG Enrichment Analysis")## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?
# Network graph of enriched terms
cnetplot(ekegg, showCategory = 10)## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Heatmap
heatplot(ekegg, showCategory = 10)
9. Transcription factors between the analysis of normal AS skin vs PsA lesion
# Load the TxDb database
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
# Extraction of exons by gene
exons_by_gene <- exonsBy(txdb, by = "gene")
# Obtain the coordinates of the genes (taking the first and last positions of the exons)
genes_info <- range(exons_by_gene)
# Obtain transcription information
transcripts_info <- transcripts(txdb)
# Map genes of interest to Entrez IDs (replace “de_genes” with the actual list of genes)
gene_entrez <- mapIds(org.Hs.eg.db, keys = mapped_genes$Symbol, column = "ENTREZID", keytype = "SYMBOL", multiVals = "first")## 'select()' returned 1:1 mapping between keys and columns# Filter the genes of interest
promoters_info <- subset(genes_info, names(genes_info) %in% gene_entrez)
# Define promoter regions (2 kb upstream of TSS)
promoters <- promoters(promoters_info, upstream = 2000, downstream = 0)## Warning in valid.GenomicRanges.seqinfo(x, suggest.trim = TRUE): GRanges object contains 1 out-of-bound range located on sequence
## chr2_GL383522v1_alt. Note that ranges located on a sequence whose
## length is unknown (NA) or on a circular sequence are not considered
## out-of-bound (use seqlengths() and isCircular() to get the lengths and
## circularity flags of the underlying sequences). You can use trim() to
## trim these ranges. See ?`trim,GenomicRanges-method` for more
## information.# Filter the main chromosomes
promoters <- keepStandardChromosomes(promoters, pruning.mode = "coarse")
# Remove any regions that exceed the limits of the chromosomes.
promoters <- trim(promoters)
# Convert CompressedGRangesList promoters to GRanges
promoters_gr <- unlist(promoters)
# Load the JASPAR grounds database
motifs <- getMatrixSet(JASPAR2020, opts = list(species = "Homo sapiens"))
# Create a data frame with the IDs of the reasons and their names (TFs)
motif_info <- data.frame(
motif_id = names(motifs),
tf_name = sapply(motifs, function(x) x@name),
stringsAsFactors = FALSE
)
# Enriching motifs in promoting regions
motifHits <- matchMotifs(motifs, promoters_gr, genome = BSgenome.Hsapiens.UCSC.hg38)
# Count the number of occurrences of the reasons in each promoting region
motif_counts <- countOverlaps(promoters_gr, motifHits)
# Add counts to GRanges with promoting regions
promoters_gr$motif_counts <- motif_counts
# Summary of motif counts
summary(promoters_gr$motif_counts)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4833 1.0000 1.0000# Obtain the names of the motifs and TFs
motif_names <- names(motifHits)
# Extract the reason table from the RangedSummarizedExperiment object
motif_data <- assays(motifHits)[[1]] # Assuming that the table of reasons is in the first list of assays
# Obtain information about TFs
# The names of the TFs can be in the colnames or in associated metadata
tf_names <- colnames(motif_data)
# Add the reason count if available
motif_counts <- rowSums(motif_data) # Count the occurrence of motifs (adjust as necessary)
# Check the dimensions of the motif_data object
dim(motif_data) # Number of rows and columns## [1] 120 633# Check the length of tf_names and motif_counts
tf_names_length <- length(tf_names)
motif_counts_length <- length(motif_counts)
# Print the lengths for diagnosis
cat("Length of tf_names:", tf_names_length, "\n")## Length of tf_names: 633cat("Length of motif_counts:", motif_counts_length, "\n")## Length of motif_counts: 120# Verify that the dimensions of motif_data correspond to the number of TFs and motifs
motif_data_dims <- dim(motif_data)
cat("Dimensions of motif_data (rows, columns):", motif_data_dims, "\n")## Dimensions of motif_data (rows, columns): 120 633# Check whether the number of reasons is greater or lesser
if (tf_names_length > motif_counts_length) {
# Check additional columns
tf_names <- tf_names[1:motif_counts_length] # Adjust to the length of motif_counts
} else {
# Adjust motif_counts to match tf_names
motif_counts <- motif_counts[1:tf_names_length]
}
# Now, I have created the data frame with adjusted lengths
motif_summary <- data.frame(
tf_name = tf_names,
motif_count = motif_counts
)
# Check the correspondence between TF names and the columns in motif_data
all(tf_names %in% colnames(motif_data)) # Should return TRUE if all TF names are present## [1] TRUE# Sort and identify key TFs
tf_summary <- motif_summary[order(motif_summary$motif_count, decreasing = TRUE), ]
top_tf_summary <- head(tf_summary, 130) # Adjust the number as required.
# Mapping Entrez IDs to gene symbols
gene_symbols <- mapIds(org.Hs.eg.db, keys = rownames(top_tf_summary), column = "SYMBOL", keytype = "ENTREZID", multiVals = "first")## 'select()' returned 1:1 mapping between keys and columns# Replace rownames with gene symbols
rownames(top_tf_summary) <- gene_symbols
top_tf_summary$motif_id <- rownames(top_tf_summary)
# Combine with the table of TF names
motif1 <- merge(top_tf_summary, motif_info, by.x = "tf_name", by.y = "motif_id")
# Reorganise columns for better viewing
motif2<- motif1[, c("motif_id", "tf_name.y", "motif_count")]
colnames(motif2) <- c("motif_id", "tf_name.y", "motif_count")
# View
print(motif2)## motif_id tf_name.y motif_count
## 1 OLR1 EN1 140
## 2 ZNF316 FOXF2 162
## 3 NAMPT FOXD1 118
## 4 PPARG FOXL1 157
## 5 SMOX FOXI1 130
## 6 PTGS2 HNF1A 130
## 7 ZC3H12A NHLH1 110
## 8 CLEC4D IRF1 101
## 9 SPON2 IRF2 126
## 10 CXCL13 MZF1(var.2) 97
## 11 GJB6 MAX::MYC 176
## 12 FAXDC2 PPARG 149
## 13 RSAD2 PAX4 140
## 14 PPARGC1A PAX6 131
## 15 AKAP13 PBX1 134
## 16 IL17F RORA 152
## 17 GBP5 RORA(var.2) 134
## 18 MUCL1 RREB1 149
## 19 NOXRED1 RXRA::VDR 120
## 20 GZMB ELK4 128
## 21 CCR7 SOX9 198
## 22 CD38 SRF 122
## 23 ACKR2 SRY 121
## 24 EFCAB13 TAL1::TCF3 159
## 25 IL1B YY1 120
## 26 CTLA4 REL 136
## 27 CYP1A1 RELA 131
## 28 ZNF483 NR1H2::RXRA 115
## 29 KANK4 NFIC::TLX1 118
## 30 TYMP ZNF354C 124
## 31 ADAMTS9 HINFP 159
## 32 NOS2 ELF5 134
## 33 IFIT3 STAT1 115
## 34 SERPINB1 REST 144
## 35 EGF CTCF 126
## 36 IGF2 GATA1::TAL1 114
## 37 IFNG STAT3 114
## 38 EGFR EWSR1-FLI1 90
## 39 ALB NFATC2 131
## 40 IL21 HNF1B 137
## 41 FCGR1A INSM1 116
## 42 SAMD9 FOXO3 161
## 43 FCGR3A RARA::RXRA 160
## 44 DKK1 NR4A2 167
## 45 CRB1 PLAG1 136
## 46 NRG1 ESR2 108
## 47 FOS ARNT::HIF1A 138
## 48 HYAL4 DUX4 136
## 49 PI3 FLI1 174
## 50 SLC7A11 FOS 144
## 51 FRZB FOSL2 122
## 52 FUT2 FOXH1 169
## 53 RORC HSF1 154
## 54 IFI6 JUN 168
## 55 GATA3 JUN(var.2) 127
## 56 GBP1 JUND(var.2) 123
## 57 IL36RN MEF2C 135
## 58 GJB2 MAF::NFE2 139
## 59 IL37 NR2C2 146
## 60 PLA2G4D NRF1 153
## 61 GPT POU2F2 119
## 62 CD274 SMAD2::SMAD3::SMAD4 129
## 63 CXCL2 STAT1::STAT2 175
## 64 ICOS TCF7L2 119
## 65 GZMA ZBTB33 167
## 66 IL2RA FOXP2 134
## 67 IL5 SREBF1 167
## 68 IL6 SREBF2 148
## 69 CXCL8 THAP1 206
## 70 CXCR2 KLF5 123
## 71 IL12B DMRT3 154
## 72 IL13 FOXG1 156
## 73 TNFRSF9 LBX1 167
## 74 IL17A NFATC3 133
## 75 CXCL10 POU6F1 115
## 76 IRS1 SHOX 143
## 77 ITGA2B ALX3 120
## 78 ACP7 BARHL2 121
## 79 MEFV BHLHE41 160
## 80 MMP1 CENPB 129
## 81 MMP9 CREB3 193
## 82 MX1 DBP 113
## 83 NKG7 ELF4 123
## 84 IL22 EN2 128
## 85 NT5C3A ESX1 137
## 86 PER1 ETV6 137
## 87 HERC6 GCM1 120
## 88 PRF1 GRHL1 163
## 89 ZNF415 GSC 126
## 90 RETN HEY2 113
## 91 CCND1 HOXC11 184
## 92 S100A8 IRF8 141
## 93 S100A9 IRF9 107
## 94 S100A12 ISX 116
## 95 S100P JDP2 106
## 96 BGLAP JDP2(var.2) 138
## 97 CCL20 KLF13 234
## 98 PRDM1 LHX6 155
## 99 SELL MEF2B 124
## 100 NOD2 MEOX1 137
## 101 CSMD1 MIXL1 111
## 102 SLC1A2 MLX 103
## 103 SOX4 MLXIPL 129
## 104 STAT1 MSC 108
## 105 SYT1 MSX1 141
## 106 TGFBR3 MYF6 163
## 107 TRAF5 NEUROD2 102
## 108 INS-IGF2 NEUROG2 87
## 109 RGPD6 NFIA 144
## 110 CALD1 NFIX 117
## 111 FOSL1 NKX2-3 117
## 112 CAPS2 NKX2-8 128
## 113 APOL1 NKX6-1 129
## 114 JRKL NKX6-2 135
## 115 HCAR3 OLIG2 125
## 116 CD8A PAX7 104
## 117 ADIPOQ POU4F2 99
## 118 CD80 SP4 85
## 119 AIM2 SPDEF 100
## 120 CD36 SPIC 188